Annual Report 2019 Centre for Image Analysis Centrum F¨Or Bildanalys

Annual Report 2019

Centre for Image Analysis

Centrum for¨ bildanalys Cover: Illustrations from the ﬁve PhD theses presented at Centre for Image Analysis (CBA) during 2019. Further information in Section 4.2.

Cover design: Leslie Solorzano

Edited by: Gunilla Borgefors, Filip Malmberg, Nadezhda Koriakina, Ingela Nystrom,¨ Ida-Maria Sintorn, Leslie Solorzano

Centre for Image Analysis, Uppsala, Sweden Contents

1 Introduction 5 1.1 General background ...... 5 1.2 CBA research ...... 6 1.3 How to contact CBA ...... 7

2 Organisation 8 2.1 Finances ...... 9 2.2 People ...... 10

3 Undergraduate education 12 3.1 Undergraduate courses ...... 12 3.2 Bachelor thesis ...... 13 3.3 Master theses ...... 14

4 Graduate education 19 4.1 Graduate courses ...... 19 4.2 Dissertations ...... 21

5 Research 25 5.1 Medical image analysis, diagnosis and surgery planning ...... 25 5.2 Microscopy: cell biology ...... 31 5.3 Microscopy: model organisms and tissues ...... 37 5.4 Mathematical and geometrical theory ...... 42 5.5 Humanities ...... 47 5.6 Cooperation partners ...... 51

6 Publications 55 6.1 Edited conference proceedings ...... 56 6.2 Journal articles ...... 56 6.3 Refereed conference proceedings ...... 71 6.4 Other ...... 75

7 Activities 77 7.1 Conference organisation ...... 77 7.2 Seminars held outside CBA ...... 78 7.3 Seminars at CBA ...... 81 7.4 Conference participation ...... 85 7.5 Non-refereed conference presentations ...... 87 7.6 Attended conferences ...... 90 7.7 Visiting scientists ...... 94 7.8 Visits to other research groups ...... 94 7.9 Committees ...... 96 1 Introduction

The Centre for Image Analysis (CBA) conducts research and graduate education in computerised image analysis and perceptualisation. Our role is to develop theory in image processing as such, but also to develop better methods, algorithms and systems for various applications. We have found applications primarily in digital humanities, life sciences, and medicine. In addition to our own research, CBA con- tributes to image technology promotion and application in other research units and society nationally as well as internationally.

1.1 General background CBA was founded in 1988 and was until 2014 a collaboration between Uppsala University (UU) and the Swedish University of Agricultural Sciences (SLU). From an organisational point of view, CBA was an independent entity within our host universities until 2010. Under the auspices of the Disciplinary Domain of Science and Technology at Uppsala University, CBA is today hosted by the Department of Information Technology (IT), where we belong to one of the five divisions, namely the Division of Visual Information and Interaction (Vi2). The organisational matters are further outlined in Section 2. A total of 38 persons within Vi2 were active in CBA research during 2019: 17 PhD students and 21 seniors (of which 3 are Professor Emeriti). Many of us have additional duties to research, for example, teaching, appointments within the Faculty, and leave for work outside academia, so the number 38 is not full-time equivalents. A complement to the CBA researchers are the 13 students who completed their Bachelor and Master thesis work with examination and/or supervision from CBA during 2019. The number of staff in the corridor fluctuates over the year thanks to that we have world class scientists visiting CBA and CBA researchers visiting their groups, for longer or shorter periods, as an important ingredient of our activities. A highlight during 2019 was that we hosted Guest Professor Fred Hamprecht from the Interdisciplinary Center for Scientific Computing, Heidelberg University, during his seven months sabbatical. Professor Hamprecht organised a much appreciated PhD course on combinatorial problems in computer vision based on exact and approximate solvers, and their relevance in the age of deep learning. He also contributed to the TissUUmaps project with novel ideas on our image-based approaches for spatial transcriptomics — a collaboration continuing after his return to Germany. A fruitful example of collaboration we have is with the Department of Surgical Sciences; Radiology, where two of our staff members work part time at the Uppsala University Hospital in order to be close to radiology researchers and also have funding from there. The activity level continues to be high, for example, there were as many as five PhD defenses during 2019, see illustrations from them on the cover-page. All our PhDs can be recognised as the main product during the years. These young well-educated persons will contribute to our society and within academia for many years to come. Another way to measure our results is to acknowledge our 33 journal papers and 14 fully reviewed conference papers. The publication results stem from a total of 42 ongoing research projects during 2019. Our projects are involving as many as 70 international labs and companies, more than 20 national collaboration partners, plus not forgetting all the local collaborations we have in Uppsala. An outward-looking activity that is particularly noteworthy is the traditional annual national symposium organised by the Swedish Society for Automated Image Analysis (SSBA) since late 1970’s, which rotates between the active universities with Chalmers hosting in March 2019. The symposium gathered about 100 participants from academia and also several companies; CBA – today Sweden’s largest academic image analysis group – had 20 participants. We are very active in international and national societies and are pleased that our leaders are recognised in these societies. Ingela Nystrom¨ continues on new positions of trust within of the International Association of Pattern Recognition (IAPR) after having been a member of the IAPR Executive Commit-

5 tee during 2008–2018 (President 2014–2016). We are also closely involved in the Network of EUropean BioImage Analysis (NEUBIAS), where Natasaˇ Sladoje and Carolina Wahlby¨ serve as members of the management committee. Nationally, CBA has two board members in the Swedish Society for Automated Image Analysis (SSBA), Ida-Maria Sintorn as Chair and Robin Strand as Vice-Chair. Other examples of national committee appointments are that Carolina Wahlby¨ serves on the Steering Group of the National Microscopy Infrastructure (NMI) and Ingela Nystrom¨ continued as Vice-Chair of the Council for Research Infra- structure (RFI) within the Swedish Research Council. During the last few years, we have been active on both national and local level to establish biomedical image analysis and biomedical engineering as more well-supported strategic research areas. The UU Faculties of Science and Technology, Medicine, and Pharmacy have formed the new centre Medtech Science and Innovation together with the UU Hospital. We are looking forward to the increased funding and collaboration opportunities we expect to be the results of this new structure. For example, in December we arranged a joint workshop between Medtech and CBA to foster collaborations and grant applications. Our image analysis support for researchers within life science continues to develop with the national SciLifeLab facility within BioImage Informatics, with Carolina Wahlby¨ as director and Pet- ter Ranefall as head. CBA has several elected members of learned socities. Ewert Bengtsson, Gunilla Borgefors, Christer Kiselman, and Carolina Wahlby¨ are elected members of the Royal Society of Sci- ences in Uppsala. Christer Kiselman is elected member and Ingela Nystrom¨ is elected as well as board member of the Royal Society of Arts and Sciences of Uppsala. In addition, Ewert Bengtsson, Gunilla Borgefors, and Carolina Wahlby¨ are elected members of the Royal Swedish Academy of Engineering Sciences (IVA), and Christer Kiselman is an elected member of the Royal Swedish Academy of Sciences. Researchers at CBA also serve on several journal editorial boards, scientiﬁc organisation boards, conference committees, and PhD dissertation committees. In addition, we take an active part in reviewing grant applications and scientiﬁc papers submitted to conferences and journals. This annual report is available in printed form as well as on the CBA webpage, see http://www.cb.uu.se/annual_report/AR2019.pdf.

1.2 CBA research The objective of CBA is to carry out research in computerised image analysis and perceptualisation. We are pursuing this objective through a large number of research projects, ranging from fundamental mathematical methods development, to application-tailored development and testing in, for example, biomedicine. We also have interdisciplinary collaboration with the humanities mainly through our projects on handwritten text recognition. In addition, we develop methods for perceptualisation, combining computer graphics, haptics, and image processing. Some of our projects lead to entrepreneurial efforts, which we interpret as a strength of our research. Our research is organised in many projects of varying size, ranging in effort from a few person months to several person years. There is a lot of interaction between different researchers; generally, a person is involved in several different projects in different constellations with internal and external partners. See Section 5 for details on and illustrations of all our research projects on the diverse topics.

6 1.3 How to contact CBA CBA maintains a home-page (http://www.cb.uu.se/). There you can ﬁnd the CBA annual report series is in existence since 1993 (approximately 100 pages each), lists of all publications since CBA was founded in 1988, and other material. Note that our seminar series is open to anyone interested. Please, join us on Mondays at 14:15. Staff members have their own homepages, which are found within the UU structure. On these, you can ﬁnd some detailed course and project information, etc.

The Centre for Image Analysis (Centrum for¨ bildanalys, CBA) can also be reached by visiting us in the corridor at Campus Polacksbacken or by sending mail. Visiting address: Lagerhyddsv¨ agen¨ 2 ITC, building 2, ﬂoor 1 Uppsala

Postal address: Box 337 SE-751 05 Uppsala SWEDEN

7 2 Organisation

In the early years, CBA was an independent entity belonging equally to Uppsala University (UU) and the Swedish University of Agricultural Sciences (SLU). However, multiple re-organisations at both universities eventually led to the current situation, where SLU is no longer involved. Since 2016, CBA is hosted by the Department of Information Technology in the Division for Visual Information and Interaction (Vi2). CBA remains Sweden’s largest single academic group for image analysis, with a strong position nationally and internationally. This successful operation shows that centre formations in special cases are worth investing in and preserving long-term. Professor Ingela Nystrom¨ is the Director of CBA since 2012. The Board of the Disciplinary Domain of Science and Technology (TekNat) has an established in- struction for CBA with description of objectives, mission, organisation, board, and roles of the director. The board consisted in 2019 of the following distinguished members (in alphabetic order):

• Teo Asplund, Dept. of Information Technology (PhD student representative – 2019-06-30)

• Anders Brun, Dept. of Information Technology

• Joel Kullberg, Dept. of Surgical Sciences; Radiology

• Nikolai Piskunov, Dept. of Physics and Astronomy (Vice-chair)

• Robin Strand, Dept. of Information Technology (adjunct in his role as Head of Division)

• Hakan˚ Wieslander, Dept. of Information Technology (PhD student representative 2019-07-01 – )

• Carolina Wahlby,¨ Dept. of Information Technology (Chair)

• Maria Agren,˚ Dept. of History

The general research subject of CBA and its PhD subject is Computerised Image Processing, including both theory and applications. More speciﬁcally, our areas of particular strength is

• Image analysis theory based on discrete mathematics

• Method development based on, e.g., machine learning in AI

• Digital humanities

• Quantitative microscopy

• Biomedical image analysis

• Visualization and haptics

As image analysis currently is finding widespread application in research in many fields as well as in society in general, we believe there is a need for a centre with an multi-disciplinary organisation. CBA offers a strong application profile based on equally strong roots in fundamental image analysis research and now reaching into the AI era. After 30 years, CBA has long experience and is more than ever at the research front.

8 2.1 Finances After the re-organisation, where CBA became part of the Department of Information Technology, the CBA economy is not separate, but integrated in activities as well as organisation within the division. Therefore, we do not report finances per se. However, from the Faculty of Science and Technology, we have a grant to CBA of 600 KSEK to be used for joint CBA initiatives. Examples are travel and accom- modation for guest researchers, work with and printing of the annual report, maintaining the website, and a percentage to the Director of CBA. Within UU, we have financial support from SciLifeLab, the Centre for Interdisciplinary Mathematics (CIM), eSSENCE (a strategic research programme in e-Science) as well as strategic funds from the IT department as a supplement to the faculty funds that came to the research program Image analysis and human-computer interaction (so-called FFF). We note that the share of external funding is increasing year by year. The funding agencies are, for example, the Swedish Research Council (VR), the Swedish Foun- dation for Strategic Research (SSF), Sweden’s Innovation Agency (VINNOVA), the European Research Council (ERC), and the Riksbankens jubileumsfond (RJ). Even though CBA as a centre does not organise undergraduate and Master education, the Division Vi2 offers programmes and several courses on Image Analysis, Computer Graphics, and Scientific Visualiza- tion as well as Human-Computer Interaction themes. Most of us teach in these courses up to 20%, and some Associate Professors in fact teach more.

9 2.2 People People afﬁliated with CBA and employed by the Department of Information Technology during 2019. In addition, there are numerous collaborators at other Departments and Universities who are afﬁliated with CBA. Information about CBA alumni is available on request from the Director of CBA.

Amin Allalou, PhD, Researcher Teo Asplund, Graduate Student Ewert Bengtsson, Professor Emeritus Karl Bengtsson Bernander, Graduate Student Gunilla Borgefors, Professor Emerita Eva Breznik, Graduate Student Anders Brun, PhD, Researcher Sukalpa Chanda, PhD, PostDoc Ashis Kumar Dhara, PhD, PostDoc Anindya Gupta, PhD, PostDoc Ankit Gupta, Graduate Student Anders Hast, Professor and Excellent Teacher Raphaela Heil, Graduate Student Christer O. Kiselman, Professor Emeritus Anna Klemm, PhD, Bioinformatician Nadezdha Koriakina, Graduate Student Joakim Lindblad, PhD, Researcher Filip Malmberg, Docent, Researcher Damian Matuszewski, Graduate Student Fredrik Nysjo,¨ Graduate Student Ingela Nystrom,¨ Professor, Director Gabriele Partel, Graduate Student Nicolas Pielawski, Graduate Student Kalyan Ram Ayyalasomayajula, Graduate Student Petter Ranefall, Docent, Bioinformatician Stefan Seipel, Professor, UU and University of Gavle¨ Ida-Maria Sintorn, Docent, Associate Professor Natasaˇ Sladoje, Docent, Associate Professor Leslie Solorzano, Graduate Student Robin Strand, Professor, Head of Division Amit Suveer, Graduate Student Ekta Vats, PhD, PostDoc Elisabeth Wetzer, Graduate Student Hakan˚ Wieslander, Graduate Student Tomas Wilkinson, Graduate Student Carolina Wahlby,¨ Professor Hangqin Zhang, PhD, PostDoc Johan Ofverstedt,¨ Graduate Student

The e-mail address of the staff is [email protected]

10 Docent Degrees from CBA 1. Lennart Thurfjell, 1999, UU 2. Ingela Nystrom,¨ 2002, UU 3. Lucia Ballerini, 2006, UU 4. Stina Svensson, 2007, SLU 5. Tomas Brandtberg, 2008, UU 6. Hans Frimmel, 2008, UU 7. Carolina Wahlby,¨ 2009, UU 8. Anders Hast, 2010, UU 9. Pasha Razifar, 2010, UU 10. Cris Luengo, 2011, SLU 11. Robin Strand, 2012, UU 12. Ida-Maria Sintorn, 2012, UU 13. Natasaˇ Sladoje, 2015, UU 14. Petter Ranefall, 2016, UU 15. Filip Malmberg, 2017, UU

CBA people appointed Excellent Teachers • Anders Hast 2014, UU

11 3 Undergraduate education CBA either supervises or reviews many Master and some Bachelor theses each year, as our subjects are useful in many different industries or for other academic research groups. They are also popular with the students. This year, we were reviewers for 14 theses, and for four of them we were also supervisors. For the rest, seven were in co-operation with industries and two together with other research groups, both needing help with image analysis applications. CBA people are also responsible for or participate in many undergraduate courses, where subjects range from Image Analysis, Computer Graphics, Machine Learning, Medical Informatics, to Programming (course examiners in bold).

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Figure 1: The number of Master theses from CBA 2001–2019.

3.1 Undergraduate courses 1. Computer Assisted Image Analysis II, 10p Natasaˇ Sladoje, Teo Asplund, Carolina Wahlby,¨ Robin Strand, Filip Malmberg, Joakim Lindblad, Anna Klemm, Fred Hamprecht, Elisabeth Wetzer, Johan Ofverstedt¨ Period: 20190101–20190331 2. User Interface Programming I, 5p Raphaela Heil Period: 20190121–20190324 3. Natural Computation Methods for Machine Learning, 10p Teo Asplund Period: 20190121–20190528 4. Computer Graphics, 10 hp Teo Asplund, Anders Hast, Filip Malmberg, Fredrik Nysjo¨ Period: 20190325–20190609

12 5. Computer-Assisted Image Analysis I, 5 hp Filip Malmberg, Joakim Lindblad, Natasaˇ Sladoje, Robin Strand, Axel Andersson, Eva Breznik, Ankit Gupta, Nadezhda Koriakina, Nicolas Pielawski, Elisabeth Wetzer Period: 20190902–20191220 6. Computer Programming I, 5p Johan Ofverstedt¨ Period: 20190902–20191103 7. Maintenance Programming, 5p Raphaela Heil Period: 20190903–20191021 8. Medical Informatics, 5p Ingela Nystrom¨ , Robin Strand Period: 20190906–20191025 9. Project in Computational Science, 15p Joakim Lindblad, Filip Malmberg, Natasaˇ Sladoje Period: 20191001–20200109 10. Advanced Software Design, 5p Raphaela Heil Period: 20191104–20200119 11. Bioinformatics for Masters Students: Getting to grips with gene expression, data mining and image analysis, 1p Ida-Maria Sintorn Period: 20191114

3.2 Bachelor thesis 1. Date: 20191114 Visual Lab Assistant: Using Augmented Reality To Aid Users In Laboratory Environments Student: Ricardo Danza Madera Supervisor: Simon Gollbo, Precisit AB, Uppsala Reviewer: Ingela Nystrom¨ Abstract: This thesis was inspired by the desire to make working in cluttered spaces easier. Laboratories are packed full of instruments and tools that scientists use to carry out experiments; this inevitably leads to an increased risk for human error as well as an often uncomfortable experience for the user. Protocols are used to carry out experiments and other processes where missteps will most likely spoil the entire experiment. How could one improve the overall experience and effectiveness in such environments? That’s when the idea of using Augmented Reality (AR) came to mind. The challenge was to be able to follow a protocol using AR. The application would require objects to be tracked in space while working and recognize which state of the process the user was in. Using OpenCV for the Computer Vision aspect of the application and writing the software in C++, it was possible to create a successful proof-of-concept. The ﬁnal result was an AR application that could track all the objects being used for the example protocol and successfully detect, and warn, when the user had made a mistake while creating a series of bacteria cultures. There is no doubt therefore, that with more time and development, a more polished product is possible. The question that is left to answer nonetheless, is whether such an application can pass a UX evaluation to determine its usability-value for users in a professional environment.

13 3.3 Master theses 1. Date: 20190417 DICOM Second Generation RT: An Analysis of New Radiation Concepts by way of First-Second Generation Conversion Student: David Holst Supervisor: Stefan Pall´ Boman, RaySearch Laboratories AB Reviewer: Robin Strand Abstract: The current DICOM communication standard for radiotherapy is outdated and has serious design issues. A new version of the standard, known as DICOM 2nd generation for Radiotherapy, has been introduced and this thesis examines new concepts relating to radiation delivery. Firstly, some background into the practice of radiotherapy is given, as well as a description of the DICOM standard. Secondly, the thesis describes the design issues of the current standard and how the 2nd generation aims to solve these. Thirdly, the thesis explores the conversion of a first generation C-Arm Photon/Electron treatment plan to the second generation RT Radiation and RT Radiation Set IODs. A converter is implemented based on a model proposed in a previous work. With some simplifications, the conversion of Basic Static and Arc treatment plans is shown to be successful. Conversion of further dynamic plan types is judged to be fairly simple to implement following the same methodology. The conversion model’s efficacy and testability are discussed and while the model is flexible and facilitates extension to further modalities some areas of improvement are suggested. Lastly, a GUI for the converter is demonstrated and the possibilities of user interaction during conversion are discussed. 2. Date: 20190502 Improving a stereo-based visual odometry prototype with global optimization Student: Felix Verpers Supervisor: Jimmy Jonsson, Saab Dynamics Reviewer: Natasaˇ Sladoje Abstract: In this degree project global optimization methods, for a previously developed software prototype of a stereo odometry system, were studied. The existing softwareestimatesthe motion between stereo frames and builds up a map of selected stereo frameswhich accumulates increasing error over time. The aim of the project was to study methods to mitigate the error accumulated over time in the step-wise motionestimation. One approach based on relative pose estimates and another approach based on reprojection optimization were implemented and evaluated for the existing platform.The results indicate that optimization based on relative keyframe estimates is promising for real-time usage. The second strategy based on reprojection of stereo triangulated points proved useful as a refinement step but the relatively small error reduction comes at an increased computational cost. Therefore, this approach requires further improvements to become applicable in situations where corrections are needed in real-time, and it is hard to justify the increased computations for the relatively small error reduction.The results also show that the global optimization primarily improves the absolute trajectory error. 3. Date: 20190702 A reliable method of tractography analysis: of DTI-data from anatomically and clinically difficult groups Student: Johanna Blomstedt Supervisor: Johanna Martensson,˚ Dept. of Surgical Sciences, UU Reviewer: Filip Malmberg Abstract: MRI is used to produce images of tissue in the body. DTI, specifically, makes it possible to track the effects of nerves where they are in the brain. This project includes a shell script and a guide for using the FMRIB Software Library, followed by StarTrack and then Trackvis in order to track difficult areas in the brain. The focus is on the trigeminal nerve (CN V). The method can be used to compare nerves in the same patient, or as a comparison to a healthy brain. 4. Date: 20190702 General image classifier for fluorescence microscopy using transfer learning Student: Hakan˚ Ohrn¨ Supervisor: Hakan˚ Wieslander Reviewer: Carolina Wahlby¨ Abstract: Modern microscopy and automation technologies enable experiments which can produce millions

14 of images each day. The valuable information is often sparse, and requires clever methods to find useful data. In this thesis a general image classification tool for fluorescence microscopy images was developed usingfeatures extracted from a general Convolutional Neural Network (CNN) trained on natural images. The user selects interesting regions in a microscopy image and then, through an iterative process, using active learning, continually builds a training data set to train a classifier that finds similar regions in other images. The classifier uses conformal prediction to find samples that, if labeled, would most improve the learned model as well as specifying the frequency of errors the classifier commits. The result show that with the appropriate choice of significance one can reach a high confidence in true positive. The active learning approach increased the precision with a downside of finding fewer examples. 5. Date: 20190826 Indoor navigation using vision-based localization and augmented reality Student: Tim Kulich Supervisor: Arno Schoonbee, Knowit Reviewer: Stefan Seipel Abstract: Implementing an indoor navigation system requires alternative techniques to the GPS. One solution is vision-based localization which takes advantage of visual landmarks and a camera to read the environment and determine positioning. Three computer vision algorithms used for pose estimation are tested and evaluated in this project in order to determine their viability in an indoor navigation system. Two algorithms, SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features), take advantage of the natural features in an image, whereas the third algorithm, ArUco, uses a manufactured marker. The evaluation displayed certain advantages for all solutions, however with the goal of using it for a navigation system ArUco was the superior solution as it performed well for key criteria, mainly computational performance and range of detection. An indoor navigation system for Android devices was developed using ArUco marker tracking for positioning and augmented reality for projecting the route. The application was able to successfully fulfill its goal of providing route guidance to a specific target location. 6. Date: 20190829 Image Based Flow Path Recognition for Chromatography Equipment Student: Ankur Shukla Supervisor: Petra Bangtsson,¨ GE Healthcare Reviewer: Robin Strand Abstract: The advancement in computer vision field with the help of deep learning methods is significant. The increase in computational resources, have lead researchers developing solutions that could help them in achieving high accuracy in image segmentation tasks. We performed segmentation of different types of objects in the chromatography instruments used in GE Healthcare, Uppsala. In this thesis project, we investigated methods in Computer vision and deep learning to segment out the different type of objects in instrument image. For a machine to automatically learn the features directly from instrument image, a deep convolutional neural network was implemented based on a recently developed existing architecture. The dataset was collected and preprocessed before using it with the neural network model. The model was trained with two different architecture Unet and Segnet developed for image segmentation. Both the used architecture is efficient and suitable for semantic segmentation tasks. Among different components to segment out in the instrument, there was a thin pipe. Unet was able to achieve good results while segmenting thin pipes with fewer data as well. Results show that Unet can act as a suitable architecture for segmenting different objects in an instrument even if we have only 100 images. Further advances can be done to improve the performance of the model by generating a better mask of the model and finding a way to collect more data for training the model. 7. Date: 20190919 Semantic and Instance Segmentation of Room Features in Floor Plans using Mask R-CNN Student: Fredrik Sandelin Supervisor: Klas Sjoberg,¨ Pythagoras AB Reviewer: Filip Malmberg Abstract: Machine learning techniques within Computer Vision are rapidly improving computers’ high- level understanding of images, thus revealing new opportunities to accomplish tasks that previously required manual intervention from humans. This paper aims to study where the current machine learning state-of-the- art is when it comes to parsing and segmenting bitmap images of floor plans. To assess the above, this paper

15 evaluates one of the state-of-the-art models within instance segmentation, Mask R-CNN, on a size-limited and challenging floor plan dataset. The model handles detecting both objects and generating a high-quality segmentation map for each object, allowing for complete image segmentation using only a single network. Additionally, in order to extend the dataset, synthetic data generation was explored, and results indicate that it aids the network with floor plan generalization. The network is evaluated on both semantic and instance segmentation metrics and results show that the network yields good, almost completely segmented floor plans on smaller blueprints with little noise, while yielding decent but not completely segmented floor plans on large blueprints with a large amount of noise. Based on the results and them being achieved from a limited dataset, Mask R-CNN shows that it has potential in both accuracy and robustness for floor plans segmentation, either as a standalone network or alternatively as part of a pipeline of several methods and techniques. 8. Date: 20190920 Automatic landmark identification in digital images of Drosophila wings for improved morphometric analysis Student: Sebastian Bas Kana˚ Supervisor: Joakim Lindblad Reviewer: Natasaˇ Sladoje Abstract: A considerable number of morphometric studies which are performed nowadays with fly wing images require manual annotation of landmarks or key-points. This work is tedious and time consuming for researchers. This is why there is interest in automating the process and therefore several approaches for this purpose have been developed. The problem with these methods is that they are difficult to use and are usually specific to a particular imaging format and species. This project’s objective is to develop two methods, one based on classic image analysis techniques, and another one based on machine learning algorithms. A comparison is made to understand the strengths of each approach and find a solution that is general and easy to use. The first method (classic) uses domain knowledge to extract features and match template structures to determine landmark locations. Every parameter is fine-tuned manually and requires a long time to develop. Nevertheless, the results achieve human-level precision. The second method uses deep learning algorithms to train 30 neural networks which divide the image into regions and extract the coordinates of the landmarks directly. The results obtained for the machine learning approach are similar (approximately 10-pixel precision for 2448 x 2048 size images), with the advantage that it does not require any domain knowledge and can be reused for any kind of format and species. A solution that combines the strengths of both methods seems to be the best path to find a fully automatic algorithm. 9. Date: 20190925 Reduction of Metal Artefacts in CT Images Using the Generative Adversarial Network CycleGAN Student: Elin Smevige Supervisor: Sebastian Andersson, RaySearch Laboratories AB Reviewer: Natasaˇ Sladoje Abstract: Techniques for metal artefact reduction (MAR) in CT images have been researched and developed for the last 40 years, with recent successes in the domain being attributed to the incorporation of deep learning methods. Previous MAR studies have relied on annotated data, but due to a lack of annotated data in the medical imaging domain, exploration of unsupervised learning methods, which train on unannotated data, motivated this work. The CycleGAN model, a member of the generative adversarial network family, is an unsupervised learning method which performs image-to-image translation from a source to target domain. In this work, the CycleGAN model is trained on data where CT images without metal artefacts compose the target domain and CT images with metal artefacts compose the source domain. The datasets consist of clinical CT images, synthesised metal artefact images, and a combination of the two datasets. The CycleGAN model is evaluated against an existing MAR technique, CNN-MAR, using the metrics: Kernel Inception Distance, Structural Similarity Index (SSIM), and a four component, gradient-based structural similarity index (4-G-SSIM). Additionally, the clinical acceptability of the models is assessed, by identifying per- centual changes in the dose calculation algorithm using the RayStation software. The CNN-MAR model outperforms the CycleGAN model for all metrics, on all datasets. Though the results of the CycleGAN performances are discouraging, further exploration of unsupervised learning methods in the MAR domian is highly encouraged. 10. Date: 20191010

16 OCR on Certificates Student: Anton Sjoberg¨ Supervisor: Albin Lundberg, Nova Cura Reviewer: Petter Ranefall Abstract: The goal of this master thesis was to create a solution which puts relevant data from scanned images of certificates into the correct database, using Optical Character Recognition (OCR). A proof of concept was done, in order to see how well this process automates. The solution is located in the system Nova Cura Flow by the company Nova Cura. The solution takes certificates that have been manually scanned, retrieves the relevant data and puts it in the correct database. All the recognition is performed in a custom connector, written in C# as a .net framework solution. First, the custom connector performs some image processing on the image, using the open source software Imagemagick among some other solutions. Some of these image processes are blurring and sharpening the image in an attempt at reducing noise. The processed image is then sent to the OCR part, which is performed by the open source software Tesseract. Here, all words in the document are retrieved, and the relevant words and the corresponding values are then extracted. This information will be the output of the custom connector. This output is then parsed through, and if the recognition for the certificate was successful, the values are put into the correct database. The solution works as a proof of concept of the fact that this process can be automated. Image quality, as well as how good the actual recognition is, are key factors in creating a good solution for this endeavor. 11. Date: 20191029 Structural representation models for multi-modal image registration in biomedical applications Student: Jo Gay Supervisor: Joakim Lindblad, Johan Ofverstedt¨ Reviewer: Natasaˇ Sladoje Abstract: In clinical applications it is often beneficial to use multiple imaging technologies to obtain information about different biomedical aspects of the subject under investigation, and to make best use of such sets of images they need to first be registered or aligned. Registration of multi-modal images is a challenging task and is currently the topic of much research, with new methods being published frequently. Structural representation models extract underlying features such as edges from images, distilling them into a common format that can be easily compared across different image modalities. This study compares the performance of two recent structural representation models on the task of aligning multi-modal biomedical images, specifically Second Harmonic Generation and Two Photon Excitation Fluorescence Microscopy images collected from skin samples. Performance is also evaluated on Bright field Microscopy images. The two models evaluated here are PCANet-based Structural Representations (PSR, Zhu et al. (2018)) and Discriminative Local Derivative Patterns (dLDP, Jiang et al. (2017)). Mutual Information is used to provide a baseline for comparison. Although dLDP in particular gave promising results, worthy of further investigation, neither method outperformed the classic Mutual Information approach, as demonstrated in a series of experiments to register these particularly diverse modalities. 12. Date: 20191113 Simplifying stereo camera calibration using M-arrays Student: Sebastian Grans Supervisor: Søren K. S. Gregersen, Technical University of Denmark Reviewer: Stefan Seipel Abstract: Digitization of objects in three dimensions, also known as 3D scanning, is becoming increasingly prevalent in all types of fields. Ranging from manufacturing, medicine, and even cultural heritage preservation. Many 3D scanning methods rely on cameras to recover depth information and the accuracy of the resulting 3D scan is therefore dependent on their calibration. The calibration process is, for the end-user, relatively cumbersome due to how the popular computer vision libraries have chosen to implement calibration target detection. In this thesis, we have therefore focused on developing and implementing a new type of calibration target to simplify the calibration process for the end-user. The calibration board that was designed is based on colored circular calibration points which form an M-array, where each local neighborhood uniquely encodes the coordinates. This allows the board to be decoded despite being occluded or partially out of frame. This contrasts the calibration board implemented in most software libraries and tool- boxes which consists of a standard black and white checkered calibration board that does not allow partial

17 views. Our board was assessed by calibrating single cameras and high FOV cameras and comparing it to regular calibration. A structured light 3D scanning stereo setup was also calibrated which was used to scan and reconstruct calibrated artifacts. The reconstructions could then be compared to the real artifacts. In all experiments, we have been able to provide similar results to the checkerboard, while also being subjectively easier to use due to the support for partial observation. We have also discussed potential methods to further improve our target in terms of accuracy and ease of use. 13. Date: 20191113 Attention P-Net for Segmentation of Post-operative Glioblastoma in MRI Student: Isabelle Enlund Astr˚ om¨ Supervisor:Ashis Kumar Dhara Reviewer: Robin Strand Abstract: Segmentation of post-operative glioblastoma is important for follow-up treatment. In this thesis, Fully Convolutional Networks (FCN) are utilised together with attention modules for segmentation of postoperative glioblastoma in MR images. Attention-based modules help the FCN to focus on relevant features to improve segmentation results. Channel and spatial attention combines both the spatial context as well as the semantic information in MR images. P-Net is used as a backbone for creating an architecture with existing bottleneck attention modules and was named attention P-Net. The proposed network and competing techniques were evaluated on a Uppsala University database containing T1-weighted MR images of brain from 12 subjects. The proposed framework shows substantial improvement over the existing techniques.

18 4 Graduate education We offer several PhD courses each year, both for our own students and for others needing image analysis tools. We also participate as guest lecturers in other PhD courses. Five PhD students successfully defended their theses this year, two in digital humanities, two in quantitative microscopy, and one in mathematical morphology theory. Two of the opponents came from Finland, one from France, one from Spain, and one from the USA.

PhD Docent 3

0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Figure 2: The number of new PhDs (orange) and Docents (brown) at CBA. CBA was founded in 1988, but did not obtain PhD examination rights in Image Analysis/Processing until 1993. The diagram shows all PhD and Docent degrees awarded by CBA so far: a total of 69 PhDs and 15 Docents.

4.1 Graduate courses 1. Classical & Modern Papers in Image Analysis, (up to) 10 hp Examiner: Natasaˇ Sladoje (2015–) Period: 20110916– Description: Presentations and discussions of classical or modern papers in image processing. The course is given continuously and organised at CBA. Participants are PhD students at CBA. 2. Deep Learning, 7.5 hp Examiner: Joakim Lindblad Period: 20180928–20190630 Description: Seminar style course on the fundamentals of Deep Learning. Participants are presenting chap- ters of the book, selected texts, or project works. Course is given continuously and organised at CBA. Participants are mainly PhD students at CBA. Main course literature: “Deep Learning”, Goodfellow, Bengio, Courville, 2016.

19 3. Elements of Deep Learning, 7.5 hp Examiner: Joakim Lindblad Period: 20190901– Description: Seminar and project style course on the Advanced concepts of Deep Learning. Participants are presenting selected topics and performing project work in groups. Course is given continuously and organised at CBA. Participants are mainly PhD students at CBA. The course continues in 2020. 4. Advanced Electron Microscopy, 5 hp Guest Lecturer: Ida-Maria Sintorn Period: 20190212–0309 Description: The course provided a general introduction to scanning and transmission electron microscopy. Lectures and labs were dedicated to special electron microscopy and focused ion beam techniques. Lecturers from the the Information Technology Center, Angstr˚ om¨ laboratory, the Biomedical Center, the Geocentrum, the Swedish University of Agricultural Sciences, and Stockholm University contributed to this course. The course was an interdisciplinary course, open to participants from all fields where electron microscopy is used. 5. Research Methodology for Information Technology (ITFM), 5 hp Lecturers: Gunilla Borgefors, Ingela Nystrom¨ Guest Lecturers: Jonas Petersson, Ulrika Haak, Librarians at the Uppsala University Library Examiner: Ingela Nystrom¨ Period: 20190322–0516 Description: The goal is to give general and useful knowledge about how to become a good and published researcher in information technology and/or various applications thereof. The first part consists of five traditional lectures on being a PhD student, practical ethics, using library resources, scientific writing, where to publish, and presenting your work orally and by poster. The second part consists of work by the participants, some in co-operation with their supervisors. Comment: 12 CBA PhD students completed the course. 6. Combinatorial Problems in Computer Vision, 5 hp Lecturer: Guest Professor Fred Hamprecht, Univeristy of Heidelberg, Germany Examiner: Carolina Wahlby¨ Period: 20190425–0620 Description: Perennial computer vision problems including instance segmentation, tracking and image par- titioning entail combinatorial optimization problems. This course formulates important computer vision problems in terms of combinatorial optimization, analyses algorithms that give exact and approximate solutions, establishes their relevance in the age of deep learning, and surveys recent literature in the field. Comment: A dozen CBA PhD students followed the course of which nine completed it.

20 4.2 Dissertations The following ﬁve PhD students successfully defended their theses in the PhD subject Computerised Image Pro- cessing during 2019.

1. Date: 20190508 Learning based Segmentation and Generation Methods for Handwritten Document Images Student: Kalyan Ram Ayyalasomayajula Supervisor: Anders Brun Assistant Supervisors: Ewert Bengtsson, Filip Malmberg Opponent: Professor Nicholas Howe, Dept. of Computer Science, Smith College, MA, USA Committee: (1) Dr Gunnar Farneback,¨ ContextVision AB, Linkoping¨ (2) Docent Ola Friman, SICK IVP AB, Linkoping¨ (3) Professor Anders Heyden, Centre for Mathematical Sciences, Lund University (4) Docent Carl Olsson, Centre for Mathematical Sciences, Lund University (5) Docent Josephine Sullivan, School of Electrical Engineering and Computer Science, KTH, Stockholm Chair: Ingela Nystrom¨ Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-513-0599-8 DiVA: http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1297042&dswid=8218 Comment: This defense was held at Uppsala University Library Carolina Rediviva. Abstract: Computerized analysis of handwritten documents is an active research area in image analysis and computer vision. The goal is to create tools that can be available for use at university libraries and for researchers in the humanities. Working with large collections of handwritten documents is very time consuming and many old books and letters remain unread for centuries. Efficient computerized methods could help researchers in history, philology and computer linguistics to cost-effectively conduct a whole new type of research based on large collections of documents. The thesis makes a contribution to this area through the development of methods based on machine learning. The passage of time degrades historical documents. Humidity, stains, heat, mold and natural aging of the materials for hundreds of years make the documents increasingly difficult to interpret. The first half of the dissertation is therefore focused on cleaning the visual information in these documents by image segmentation methods based on energy minimization and machine learning. However, machine learning algorithms learn by imitating what is expected of them. One prerequisite for these methods to work is that ground truth is available. This causes a problem for historical documents because there is a shortage of experts who can help to interpret and interpret them. The second part of the thesis is therefore about automatically creating synthetic documents that are similar to handwritten historical documents. Because they are generated from a known text, they have a given facet. The visual content of the generated historical documents includes variation in the writing style and also imitates degradation factors to make the images realistic. When machine learning is trained on synthetic images of handwritten text, with a known facet, in many cases they can even give an even better result for real historical documents.

2. Date: 20190604 Learning-based Word Search and Visualization for Historical Manuscript Images Student: Tomas Wilkinson Supervisor: Anders Brun Assistant Supervisors: Ewert Bengtsson, Anders Hast Opponent: Professor Josep Llados, Computer Vision Center, Universitat Autonoma` de Barcelona, Spain Committee: (1) Professor Jorg¨ Tiedemann, Dept. of Digital Humanities, University of Helsinki (2) Docent Jorgen¨ Ahlberg, Dept. of Electrical Engineering, Linkoping¨ University (3) Associate Professor Fredrik Heintz, Dept. of Computer and Information Science, Linkoping¨ University (4) Dr Gunnar Farneback,¨ ContextVision AB, Stockholm (substitute member) Chair: Robin Strand Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-513-0633-9 DiVA: http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1303103&dswid=-8732 Comment: This defense was held at Uppsala University Library Carolina Rediviva.

21 Abstract: Today, work with historical manuscripts is nearly exclusively done manually, by researchers in the humanities as well as laypeople mapping out their personal genealogy. This is a highly time consuming endeavour as it is not uncommon to spend months with the same volume of a few hundred pages. The last few decades have seen an ongoing effort to digitise manuscripts, both preservation purposes and to increase accessibility. This has the added effect of enabling the use methods and algorithms from Image Analysis and Machine Learning that have great potential in both making existing work more efﬁcient and creating new methodologies for manuscript-based research. The ﬁrst part of this thesis focuses on Word Spotting, the task of searching for a given text query in a manuscript collection. This can be broken down into two tasks, detecting where the words are located on the page, and then ranking the words according to their similarity to a search query. We propose Deep Learning models to do both, separately and then simultaneously, and successfully search through a large manuscript collection consisting of over a hundred thousand pages. A limiting factor in applying learning-based methods to historical manuscript images is the cost, and therefore, lack of annotated data needed to train machine learning models. We propose several ways to mitigate this problem, including generating synthetic data, augmenting existing data to get better value from it, and learning from pre-existing, partially annotated data that was previously unusable. In the second part, a method for visualising manuscript collections called the Image-based Word Cloud is proposed. Much like it text-based counterpart, it arranges the most representative words in a collection into a cloud, where the size of the words are proportional to their frequency of occurrence. This grants a user a single image overview of a manuscript collection, regardless of its size. We further propose a way to estimate a manuscripts production date. This can grant historians context that is crucial for correctly interpreting the contents of a manuscript.

3. Date: 20190605 Methods for Processing and Analysis of Biomedical TEM Images Student: Amit Suveer Supervisor: Ida-Maria Sintorn Assistant Supervisors: Natasaˇ Sladoje, Carolina Wahlby¨ Opponent: Professor Jari Hyttinen, BioMediTech, Tampere University of Technology, Finland Committee: (1) Assoc. Professor Vedrana Andersen Dahl, Dept. of Applied Mathematics and Computer Science, Tech- nical University of Denmark, Lyngby, Denmark (2) Professor Fred A. Hamprecht, Dept. of Physics and Astronomy, Heidelberg University, Germany (3) Associate Professor Sabine Leh, Dept. of Clinical Medicine, University of Bergen, Norway (4) Professor Klaus Leifer, Dept. of Engineering Sciences, UU (5) Assoc. Professor Kevin Smith, School of Electrical Engineering and Computer Science, KTH, Stock- holm Chair: Ingela Nystrom¨ Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-513-0653-7 DiVA: http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1305491&dswid=-9024 Abstract: Transmission Electron Microscopy (TEM) has the high resolving capability and high clinical significance; however, the current manual diagnostic procedure using TEM is complicated and time-consuming, requir- ing rarely available expertise for analyzing TEM images of the biological specimen. This thesis addresses the bottlenecks of TEM-based analysis by proposing image analysis methods to automate and improve critical time-consuming steps of currently manual diagnostic procedures. The automation is demonstrated on the computer-assisted diagnosis of Primary Ciliary Dyskinesia (PCD), a genetic condition for which TEM analysis is considered the gold standard. The methods proposed for the automated workflow mimic the manual procedure performed by the pathologists to detect objects of interest – diagnostically relevant cilia instances – followed by a computational step to combine information from multiple detected objects to enhance the important structural details. The workflow includes an approach for efficient search through a sample to identify objects and locate areas with a high density of objects of interest in low-resolution images, to perform high-resolution imaging of

22 the identiﬁed areas. Subsequently, high-quality objects in high-resolution images are detected, processed, and the extracted information is combined to enhance structural details. This thesis also addresses the challenges typical for TEM imaging, such as sample drift and deformation, or damage due to high electron dose for long exposure times. Two alternative paths are investigated: (i) different strategies combining short exposure imaging with suitable denoising techniques, including conventional approaches and a proposed deep learning based method, are explored; (ii) conventional interpolation approaches and a proposed deep learning based method are analyzed for super-resolution reconstruction using a single image. For both explored directions, in the best case scenario, the processing time is nearly 20 times faster as compared to the acquisition time for a single long exposure high illumination image. Moreover, the reconstruction approach (ii) requires nearly 16 times lesser data (storage space) and overcomes the need for high-resolution image acquisition. Finally, the thesis addresses critical needs to enable objective and reliable evaluation of TEM image denoising approaches. A method for synthesizing realistic noise-free TEM reference images is proposed, and a denoising benchmark dataset is generated and made publicly available. The proposed dataset consists of noise-free references along with masks encompassing the critical diagnostic structures. This enables performance evaluation based on the capability of denoising methods to preserve structural details, instead of merely grading them based on the signal to noise ratio improvement and preservation of gross structures.

4. Date: 20191206 Precise Image-based Measurements through Irregular Sampling Student: Teo Asplund Supervisor: Robin Strand Assistant Supervisors: Gunilla Borgefors, Cris L. Luengo Hendriks, Matthew J. Thurley Opponent: Professor Hugues Talbot, CentraleSupelec,´ Universite´ Paris-Saclay, France Committee: (1) Enseignant-Chercheur Jean Cousty, ESIEE Paris, France (2) Professor Anders Heyden, Centre for Mathematical Sciences, Lund University (3) Professor Heikki Kalvi¨ ainen,¨ School of Engineering Science, Lappeenranta-Lahti University of Tech- nology, Finland (4) Professor Maya Neytcheva, Dept. of Information Technology, UU (substitute member) Chair: Ingela Nystrom¨ Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-513-0783-1 DiVA: http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1361810&dswid=-8486 Abstract: Mathematical morphology is a theory that is applicable broadly in signal processing, but in this thesis we focus mainly on image data. Fundamental concepts of morphology include the structuring element and the four operators: dilation, erosion, closing, and opening. One way of thinking about the role of the structuring element is as a probe, which traverses the signal (e.g. the image) systematically and inspects how well it ”fits” in a certain sense that depends on the operator. Although morphology is defined in the discrete as well as in the continuous domain, often only the discrete case is considered in practice. However, commonly digital images are a representation of continuous reality and thus it is of interest to maintain a correspondence between mathematical morphology operating in the discrete and in the continuous domain. Therefore, much of this thesis investigates how to better approximate continuous morphology in the discrete domain. We present a number of issues relating to this goal when applying morphology in the regular, discrete case, and show that allowing for irregularly sampled signals can improve this approximation, since moving to irregularly sampled signals frees us from constraints (namely those imposed by the sampling lattice) that harm the correspondence in the regular case. The thesis develops a framework for applying morphology in the irregular case, using a wide range of structuring elements, including non-flat structuring elements (or structuring functions) and adaptive morphology. This proposed framework is then shown to better approximate continuous morphology than its regular, discrete counterpart. Additionally, the thesis contains work dealing with regularly sampled images using regular, discrete morphology and weighting to improve results. However, these cases can be interpreted as specific instances of irregularly sampled signals, thus naturally connecting them to the overarching theme of irregular sampling, precise measurements, and mathematical morphology.

23 5. Date: 20191214 Image and Data Analysis for Biomedical Quantitative Microscopy Student: Damian Matuszewski Supervisor: Ida-Maria Sintorn Assistant Supervisor: Carolina Wahlby¨ Opponent: Professor Peter Horvath, Institute for Molecular Medicine Finland (FIMM), Helsinki, Finland Committee: (1) Professor Irene Gu, Dept. of Electrical Engineering, Chalmers University of Technology, Goteborg¨ (2) Professor Mats Gustafsson, Dept. of Medical Sciences, UU (3) Associate Professor Carl Nettelblad, Dept. of Information Technology, UU (4) Dr Simon Flyvbjerg Noerrelykke, Dept. of Information Technology and Electrical Engineering, ETH Zurich,¨ Switzerland (5) Professor Paivi¨ Ostling,¨ Dept. of Oncology and Pathology, Karolinska Institutet, Solna Chair: Ingela Nystrom¨ Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-513-0771-8 DiVA: http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1359483&dswid=9676 Abstract: This thesis presents automatic image and data analysis methods to facilitate and improve microscopy-based research and diagnosis. New technologies and computational tools are necessary for handling the ever- growing amounts of data produced in life science. The thesis presents methods developed in three projects with different biomedical applications. In the first project, we analyzed a large high-content screen aimed at enabling personalized medicine for glioblastoma patients. We focused on capturing drug-induced cell-cycle disruption in fluorescence microscopy images of cancer cell cultures. Our main objectives were to identify drugs affecting the cell-cycle and to increase the understanding of different drugs’ mechanisms of action. Here, we present tools for automatic cell-cycle analysis and identification of drugs of interest and their effective doses. In the second project, we developed a feature descriptor for image matching. Image matching is a central pre-processing step in many applications. For example, when two or more images must be matched and registered to create a larger field of view or to analyze differences and changes over time. Our descriptor is rotation-, scale-, and illumination-invariant and it has a short feature vector which makes it computationally attractive. The flexibility to combine it with any feature detector and the customization possibility make it a very versatile tool. In the third project, we addressed two general problems for bridging the gap between deep learning method development and their use in practical scenarios. We developed a method for convolutional neural network training using minimally annotated images. In many biomedical applications, the objects of interest cannot be accurately delineated due to their fuzzy shape, ambiguous morphology, image quality, or the expert knowledge and time it requires. The minimal annotations, in this case, consist of center-points or centerlines of target objects of approximately known size. We demonstrated our training method in a challenging application of a multi-class semantic segmentation of viruses in transmission electron microscopy images. We also systematically explored the influence of network architecture hyper-parameters on its size and performance and show the possibility to substantially reduce the size of a network without compromising its performance. All methods in this thesis were designed to work with little or no input from biomedical experts but of course, require fine-tuning for new applications. The usefulness of the tools has been demonstrated by collaborators and other researchers and has inspired further development of related algorithms.

24 5 Research In this section, we briefly present our research, loosely grouped in 41 projects. These projects are of very different sizes and longevity, and many partly merge with each other. The majority have total or partial external funding. In Section 5.1, we list eight ongoing projects researching medical applications that concern the whole body or whole organs, together with surgical planning. This uses many different imaging modalities and the tools used are 3D image analysis, haptics, and visualization. In Section 5.2, we list nine projects that analyse cells, viruses, or proteins using a microscope, often in a time-series. Also in Section 5.3, we list seven projects using microscopic images, but here the emphasis is on whole tissues or whole organisms. The most used model organism is the zebrafish. In Section 5.4, we list eleven theoretical projects that are not aimed at a particular application, but improve image analysis methods in general. Our research is to a high extent driven by advanced applications which call for theoretically well founded generally applicable image analysis methods, always in a very high demand. Two of these projects are new, while one survives almost from the start of CBA. Finally, in Section 5.5, we list six projects in digital humanities. One is new and five investigate the possibilities of analysing handwritten texts using image processing and pattern recognition. In Section 5.6, we have collected all our research partners — internationally, nationally, and locally in Uppsala — with whom we had active cooperation during 2019.

5.1 Medical image analysis, diagnosis and surgery planning 1. Imiomics — Large-Scale Analysis of Medical Volume Images Robin Strand, Filip Malmberg, Eva Breznik Partner: Joel Kullberg, Hakan˚ Ahlstrom,¨ Dept. of Surgical Sciences, Radiology, UU Funding: Faculty of Medicine, UU; Swedish Research Council grant 2016-01040; AstraZeneca Period: 20120801– Abstract: Magnetic resonance tomography (MR) images are very useful in clinical use and in medical research, e.g., for analyzing the composition of the human body. At the division of Radiology, UU, a huge amount of MR data, including whole body MR images, is acquired for research on the connection between the composition of the human body and disease. To do compare volume images voxel by voxel, we develop a large scale analysis method, enabled by image registration methods. These methods utilize, for example, segmented tissue and anatomical landmarks. Based on this idea, we have developed Imiomics (imaging omics) – an image analysis concept that allows statistical and holistic analysis of whole-body image data. The Imiomics concept is holistic in three respects: (i) The whole body is analyzed, (ii) All collected image data is used in the analysis and (iii) It allows integration of other collected non-imaging patient information in the analysis. During 2019, we continued the work on improving the registration method. We also published articles where Imiomics was used to ﬁnd associations between image data and (i) the metabolic syndrome, (ii) vasodilation and (iii) adipose and lean tissue distribution. In addition, we evaluated Imiomics in anomaly detection in malignant disease. See Figure 3.

2. HASP: Haptics-Assisted Surgery Planning Ingrid Carlbom, Pontus Olsson, Fredrik Nysjo,¨ Johan Nysjo,¨ Ingela Nystrom¨ Partner: Daniel Buchbinder, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Andreas Thor, Johanna Nilsson, Dept. of Surgical Sciences, Oral & Maxillofacial Surgery, UU Hospital; Andres Rodriguez Lorenzo, Dept. of Surgical Sciences, Plastic Surgery, UU Hospital Funding: BIO-X; Thureus´ Stiftelsen, TN faculty, MF faculty, UU Period: 20150101– Abstract: The goal of HASP, our haptics assisted surgery planning system, is to put the planning process for complex head and neck surgery into the hands of the surgeon. In the last years, the focus has been on evaluating HASP and the BoneSplit segmentation software both at the Uppsala University Hospital and at Mount Sinai Beth Israel in NYC. At Mount Sinai Beth Israel, this has resulted in a validation study of the

25 Figure 3: Imiomics - Large-Scale Analysis of Medical Volume Images

accuracy of HASP with 12 retrospective cases and eight prospective cases. For each case, we produce a neomandible from resin models generated by a 3D printer of the mandible, cutting guides, ﬁbula, and case- speciﬁc plates, that are cut and glued together. CT models of the reconstructed resin neomandible were compared with the HASP neomandible, to verify their correspondence. The study is currently in process of being submitted. At the UU Hospital, a trauma study has been evaluated for the haptic model in HASP on CT data from a scanned plastic skull and ten retrospective cases. For the plastic skull, we compared accuracy and precision between users, whereas for the retrospective cases we compared precision only. The study is submitted for publication. See Figure 4.

Figure 4: HASP: Haptics-Assisted Surgery Planning

3. Image Processing for Virtual Design of Surgical Guides and Plates Fredrik Nysjo,¨ Ludovic Blache, Filip Malmberg, Ingrid Carlbom, Ingela Nystrom¨ Partner: Andreas Thor; Uppsala University Hospital; Andres Rodriguez Lorenzo, Uppsala University Hos- pital; Daniel Buchbinder, Mt Sinai-Beth Israel Hospital, New York, Pontus Olsson, Savantic AB, Stockholm Period: 20150317– Abstract: An important part of virtual planning for reconstructive surgery, such as cranio-maxillofacial (CMF) surgery, is the design of customized surgical tools and implants. In this project, we are looking into how distance transforms and constructive solid geometry can be used to generate 3D printable models of surgical guides and plates from segmented computed tomography (CT) images of a patient, and how the accuracy and precision of the modelling can be improved using grayscale image information in combination with anti-aliased distance transforms. Another part of the project is to develop simple and interactive tools that allow a surgeon to create such models. Previously, we implemented a set of design tools for bone reconstruction in our existing surgery planning system HASP. When removing a tumor, a soft tissue defect in the face is also created. To reconstruct this defect, vascularized tissue is usually transplanted from other parts of the body. We developed a method to estimate the shape and dimensions of soft tissue resections

26 from CT data, and a sketch-based interface for the surgeon to paint the resection contour on the patient. We also investigated numerical ﬁnite element models to simulate the non-rigid behavior of the soft tissue ﬂap during the reconstruction. See Figure 5.

Figure 5: Image Processing for Virtual Design of Surgical Guides and Plates

4. Interactive deep learning segmentation for decision support in neuroradiology Ashis Kumar Dhara, Robin Strand, Filip Malmberg Partner: Johan Wikstrom¨ and Elna-Marie Larsson, Dept. of Surgical Sciences, Radiology, UU Funding: Swedish Research Council, AIDA Period: 20150501– Abstract: Many brain diseases can damage brain cells (nerve cells), which can lead to loss of nerve cells and, secondarily, loss of brain volume. Technical imaging advancements allow detection and quantification of very small tissue volumes in magnetic resonance (MR) neuroimaging. Due to the enormous amount of information in a typical MR brain volume scan interactive tools for computer aided analysis are absolutely essential for this task. Available interactive methods are often not suited for this problem. Deep learning by convolution neural networks has the ability to learn complex structures from training data. We develop, analyze and evaluate interactive deep learning segmentation methods for quantification and treatment response analysis in neuroimaging. Interaction speed is obtained by dividing the segmentation procedure into an offline pre-segmentation step and an on-line interactive loop in which the user adds constraints until sat- isfactory result is obtained. The overarching aim is to allow detailed correct diagnosis, as well as accurate and precise analysis of treatment response in neuroimaging, in particular in quantification of intracranial aneurysm remnants and brain tumor growth. In 2019, we developed attention-based learning methods. Results were presented at SSDL. See Figure 6. 5. Methods for Combined MR and Radiation Therapy Equipment Robin Strand Partner: Anders Ahnesjo,¨ David Tilly, Dept. of Immunology, Genetics and Pathology, UU. Samuel Frans- son, Hakan˚ Ahlstrom,¨ Dept. of Surgical Sciences, Radiology, UU Funding: VINNOVA; Barncancerfonden; TN-faculty, UU Period: 20160601– Abstract: Uppsala University and Hospital are current investing in image guided radiotherapy. An important component in the strategy is a combined MR scanner and treatment unit, enabling MR imaging right before and during treatment making it possible to adjust for internal motion. In this project, we develop methods for fast detection and quantification of motion for real-time adjustment of the radiation therapy in the combined MR scanner and treatment unit. A manuscript on the use of a motion model in radiation therapy was finalized and submitted. See Figure 7.

27 Figure 6: Interactive deep learning segmentation for decision support in neuroradiology

Figure 7: Methods for Combined MR and Radiation Therapy Equipment

6. Statistical Considerations in Whole-Body MR Analyses Eva Breznik, Robin Strand, Filip Malmberg Partner: Joel Kullberg, Hakan˚ Ahlstrom,¨ Division of Radiology, Dept. of Surgical Sciences, UU Funding: Centre for Interdisciplinary Mathematics, CIM, UU; TN-Faculty, UU Period: 201609– Abstract: In this project, the focus is on testing and developing methods for Imiomics, to facilitate utilization of whole-body MR images for medical purposes. For inference about activated areas, present in the image, statistical tests are done on series of images at every voxel. This introduces accuracy and reliability problems when drawing conclusions regarding the images or multi-voxel areas as a whole, due to the large number of tests that are considered at the same time. The solution to this problem is a proper multiple testing correction method. Therefore we need to test the existing ones on our speciﬁc datasets and explore possibilities of new ones, speciﬁcally tailored to our problem. Results have been in part presented at SSBA 2017 in Linkoping.¨ A manuscript has been submitted and is currently under review. See Figure 8.

7. Abdominal organ segmentation Eva Breznik, Robin Strand, Filip Malmberg Partner: Joel Kullberg, Hakan˚ Ahlstrom,¨ Division of Radiology, Dept. of Surgical Sciences, UU Funding: Centre for Interdisciplinary Mathematics, CIM, UU; TN-Faculty, UU Period: 201706– Abstract: We focus on improving the existing registration method for whole-body scans by including segmentation results as prior knowledge. Segmentation of the organs in the abdomen is a daunting task, as the organs vary a lot in their properties and size. And having a robust method to segment a number of

28 Figure 8: Statistical Considerations in Whole-Body MR Analyses

them would not only be useful in clinical setting, but it could also help guide the registration method in those areas, which are most challenging to register. To develop an appropriate method we apply convolutional neural networks and explore ways of including prior knowledge in the process (via better sampling strategies and direct injection of anatomical information with landmarks and patch locality). Preliminary results on improvements we achieved by integrating anatomical (spatial) knowledge within a fully convolutional network were presented at SSDL 2018 in Goteborg.¨ A manuscript, comparing injections of spatial knowledge into the network at various stages is in preparation. See Figure 9.

Figure 9: Abdominal organ segmentation

8. Deep learning and explainable artiﬁcial intelligence for oral cancer detection Nadezhda Koriakina, Joakim Lindblad, Natasaˇ Sladoje, Ewert Bengtsson Partner: Eva Darai Ramqvist - Pathology and Cytology, Karolinska Institute, Stockholm, Sweden; Jan- Michael Hirsch - Surgical Sciences, Oral & Maxillofacial Surgery, Uppsala University, Uppsala, Sweden; Christina Runow Stark - Public Dental Service, Sodersjukhuset,¨ Stockholm, Sweden Funding: Swedish Research Council; VINNOVA through MedTech4Health, AIDA; TN-faculty, UU; Period: 20181001– Abstract: Oral cancer is one of the most common malignancies in the world. It is noteworthy that the oral cavity can be relatively easily accessed for routine noninvasive screening tests that could potentially decrease the incidence of this type of cancer. Deep Convolutional Neural Networks (DCNNs) show promising ability for detection of subtle precancerous changes at a very early stage, however, their opacity does not allow to infer how they arrive at a decision. In order to develop a reliable algorithm for oral cancer

29 detection, we are examining methods from the emerging field of eXplainable Artificial Intelligence (XAI) that could elevate understanding of the DCNNs’ classification properties. In addition, during this project we are aiming to increase understanding of precancerous malignancy associated changes (MACs). Although the biological nature of MACs is not fully understood, the consistency of morphology and textural changes within a cell dataset could shed light on the premalignant state. We envision the screening system to provide diagnostic support to cytopathologists, therefore, their opinion and expertise will be reflected throughout the project. Another research track, potentially useful for the intended medical application, is towards accurate estimation and presentation of classification certainty of the DCNN. See Figure 10.

Figure 10: Deep learning and explainable artiﬁcial intelligence for oral cancer detection

30 5.2 Microscopy: cell biology

9. A smart and easy platform to facilitate ultrastructural pathologic diagnoses Amit Suveer, Natasaˇ Sladoje, Joakim Lindblad, Ida-Maria Sintorn Partner: Vironova AB; Anca Dragomir - Uppsala Academic Hospital; Kjell Hultenby - Karolinska Institutet Funding: MedTech4Health, VINNOVA; TN-faculty, UU Period: 20160109– Abstract: TEM is an essential diagnostic tool for screening human tissues at the nanometer scale. It is the only option in some cases and considered as gold standard for diagnosing several disorders, e.g. cilia and renal diseases, rare cancers to name a few. The high resolution of TEM provides unique morphological information, signiﬁcant for diagnosis and personalized care management. However, the microscope is expensive, technically complex, bulky, needs a high level of expertise to operate, and still diagnosis is subjec- tive and time-consuming. In this project we are collaborating with microscope manufacturers, pathologists, and microscopists, to develop the next generation smart software and easy platform that will signiﬁcantly simplify and enhance the TEM imaging and analysis experience. The work includes automated steering of a TEM microscope for the search for regions of interest, followed by automatic multiscale imaging and processing of the images of acquired regions. During 2018 we have continued development of the analysis methods in close collaboration with UAS and KS. Results have been presented at: IEEE International Symposium on Biomedical Imaging, USA; Swedish Symposium on Image Analysis, Stockholm, Sweden; Summer School on Image Processing, Graz, Austria; Swedish Symposium on Deep Learning, Goteborg;¨ European Conference on Computer Vision, Munich, Germany. See Figure 11.

Figure 11: A smart and easy platform to facilitate ultrastructural pathologic diagnoses

10. Advanced Methods for Reliable and Cost Efficient Image Processing in Life Sciences Natasaˇ Sladoje, Joakim Lindblad, Ewert Bengtsson, Ida-Maria Sintorn Partner: Buda Bajic,´ Marija Delic,´ Faculty of Technical Sciences, University of Novi Sad, Serbia; Tomas Majtner, University of Southern Denmark, Odense, Denmark Funding: VINNOVA; UU TN-faculty; Swedish Research Council Period: 201308—- Abstract: Within this project our goal is to increase reliability, efficiency, and robustness against variations in sample quality, of computer assisted image analysis. We have been focused on two particular applications: (1) Chromatin distribution analysis for cervical cancer diagnostics, and (2) Virus detection and recognition in TEM images. In 2019, we considered also classification of HEp-2 images. Efficient utilization of available image data to characterize barely resolved structures, is crucial in all the considered applications. We have been focused on development of methods which enable preservation and efficient usage of information, aggregate information of different types, improve robustness of the developed methods and increase precision of the analysis results. During 2019, we have studied the potential of Deep Convolutional Genera- tive Adversarial Networks (DCGANs) to generate training data - i.e., enable data augmentation - necessary for successful classification of biomedical images, where annotated data is usually lacking. Even though we successfully generated synthetic data of a high visual quality, we have also shown that application of DCGAN for classification purposes does not lead to convincing results, in particular when the generated

31 images are used independently, without the combination with original ones. Our results are presented at the SCIA 2019 conference. See Figure 12.

Figure 12: Advanced Methods for Reliable and Cost Efﬁcient Image Processing in Life Sciences

11. NEUBIAS (Network of EUropean BioImage AnalystS) - COST Action 15124 Natasaˇ Sladoje, Joakim Lindblad, Carolina Wahlby,¨ Petter Ranefall, Anna Klemm, Elisabeth Wetzer, Nadezhda Koriakina Partner: NEUBIAS network with more than 240 members from more than 40 countries Funding: EU Framework Programme Horizon 2020 Period: 20160503– Abstract: This COST Action aims to provide a stronger identity to BioImage Analysts by organising different types of interactions between Life scientists, BioImage analysts, microscopists, developers and private sector. It collaborates with European Imaging research infrastructures to set up best practice guidelines for Image Analysis (IA). The Action successfully works on creating an interactive database for BioImage analysis tools and workﬂows with annotated image sample datasets, to help matching practical needs in biological problems with software solutions. It implements a benchmarking platform for these tools. To increase the overall level of IA expertise in the LSc, the Action proposes a novel training programme with three levels of courses, releasing of open textbooks, and offering of a short term scientiﬁc missions programme to foster collaborations, IA-technology access, and knowledge transfer for scientists and specialists lacking these means. We have been actively participating in different activities organised within NEUBIAS network. We were engaged as work group leaders, teachers, taggers, invited speakers at NEUBIAS workshops and symposia, and as Management Committee members. We have strengthened our collaboration with bioimage analysts from more than 40 NEUBIAS member-countries. See Figure 13.

Figure 13: NEUBIAS (Network of EUropean BioImage AnalystS) - COST Action 15124

12. HASTE: Hierarchical Analysis of Spatial and Temporal Data Carolina Wahlby,¨ Hakan˚ Wieslander, Ankit Gupta Partner: Andreas Hellander, Salman Toor, Ben Blamey, Dept. of Information Technology, UU; Ola Spjuth, Phil Harrison, Dept. of Pharmaceutical Biosciences, UU; Markus M. Hilscher, SciLifeLab, Dept. of Bio- chemistry and Biophysics, Stockholm University; Ida-Maria Sintorn, Vironova AB; Lars Carlsson, Johan Karlsson, Alan Sabirsh, Ola Engkvist, AstraZeneca AB: Mats Nilsson, Stockholm University Funding: Swedish Foundation for Strategic Research (SSF) Period: 20170103– Abstract: Images contain very rich information, and digital cameras combined with image processing and analysis can detect and quantify a range of patterns and processes. The valuable information is however often sparse, and the ever increasing speed at which data is collected results in data-volumes that exceed the computational resources available. The HASTE project takes a hierarchical approach to acquisition,

32 analysis, and interpretation of image data. We develop computationally efficient measurements for data description, confidence-driven machine learning for determination of interestingness, and a theory and framework to apply intelligent spatial and temporal information hierarchies, distributing data to computational resources and storage options based on low-level image features. At Vi2 we focus on developing efficient measurements that will identify non-informative data early on in the analysis process; either online at data collection, or off-line prior to full data analysis. The challenge is to use minimal computational time and power to extract a broad range of informative measurements from spatial-, temporal-, and multi-parametric image data, useful as input for conformal predictions and efficient enough to work well in a streaming setting. Examples include drug localization in lung tissue, time lapse experiment outcome prediction and learning from few training examples. See Figure 14.

Figure 14: HASTE: Hierarchical Analysis of Spatial and Temporal Data

13. Multi-layer object representations for integrated shape and texture analysis with applications in biomedical image processing Elisabeth Wetzer, Natasaˇ Sladoje, Joakim Lindblad Partner: Ida-Maria Sintorn,Vironova AB; Christina Runow Stark, FTV Stockholm AB Funding: Centre for Interdisciplinary Mathematics, TN-Faculty, UU, VINNOVA through MedTech4Health Period: 20171001– Abstract: The aim of the project is to develop the theoretical foundation for a class of methods applicable to multi-layered heterogeneous object representations and to apply and evaluate these methods in clinical biomedical applications. In 2019 the focus laid on the fusion of texture descriptors and convolutional neural networks (CNN). CNNs are, in a number of different ways, either combined with information extracted from Local Binary Pattern (LBP) features, or include a module within the network, designed to extract LBP-like features to provide a powerful tool for texture-based classiﬁcation of biomedical data. A number of approaches are tested and evaluated on an oral cancer dataset for which the possibilities for a nationwide screening program for early-stage cancer detection are being investigated. The project is carried out in collaboration with Christina Runow Stark, FTV Stockholm AB, who has provided the dataset. This work has resulted in the poster on “Texture-Based Oral Cancer Detection: A Performance Analysis of Deep Learn- ing Approaches” presented at the NEUBIAS Symposium in Luxembourg and a presentation at SSBA in Goteborg,¨ as well as the paper “When Texture Matters: Texture-Focused CNNs outperform general data augmentation and pretraining in Oral Cancer Detection”, which will be presented at ISBI 2020 in Iowa. See Figure 15.

33 Figure 15: Multi-layer object representations for integrated shape and texture analysis with applications in biomedical image processing

14. Image- and AI-based cytological cancer screening Joakim Lindblad, Ewert Bengtsson, Carolina Wahlby,¨ Nadezhda Koriakina Partner: Christina Runow Stark - Medicinsk Tandvard,˚ Folktandvarden˚ AB, Stockholm; Eva Ramquist - Karolinska University Hospital; Jan-Michael´ Hirsch - Medicinsk Tandvard,˚ Sodersjukhuset;¨ Kunjuraman Sujathan - Regional Cancer Centre, Kerala, India Funding: VINNOVA through MedTech4Health, AIDA Period: 20171001– Abstract: Oral cancer incidence is rapidly increasing worldwide, with over 450,000 new cases found each year. The most effective way of decreasing cancer mortality is early detection, which makes routine screening of patient risk groups highly desired. Within this project, we aim to develop a system that uses artificial intelligence (AI) to automatically detect oral cancer in microscopy images of brush samples, which can quickly and without pain be routinely taken at ordinary dental clinics. We expect that the proposed approach will be crucial for introducing a screening program for oral cancer at dental clinics, in Sweden and the world. The project involves researchers from Uppsala University, Karolinska University Hospital, Folk- tandvarden˚ Stockholms lan¨ AB, and the Regional Cancer Center in Kerala, India. The project has been very successful and we are therefore continuing with intensified efforts. The in 2020 started AIDA project on trustworthy AI-based decision support is a direct consequence of observed needs for the further integration and adoption of our reached results and developed methods. During 2019 we have among other things presented our work at NEUBIAS, SWEDENTAL and SSBA conferences and prepared a manuscript ”A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images”. See Figure 16.

Figure 16: Image- and AI-based cytological cancer screening

34 15. COMULIS (Correlated Multimodal Imaging in Life Sciences) - COST Action 17121 Natasaˇ Sladoje, Joakim Lindblad Partner: COMULIS network with more than 200 members from 36 countries Funding: EU Framework Programme Horizon 2020 Period: 20181012– Abstract: COMULIS is an EU-funded COST Action that aims at fueling urgently needed collaborations in the ﬁeld of correlated multimodal imaging (CMI), promoting and disseminating its beneﬁts through showcase pipelines, and paving the way for its technological advancement and implementation as a versatile tool in biological and preclinical research. CMI combines two or more imaging modalities to gather information about the same specimen and to create a composite view of the sample with multidimensional information about its macro-, meso- and microscopic structure, dynamics, function and chemical composition. No single imaging technique can reveal all these details; CMI is the only way to understand biomedical processes and diseases holistically. CMI relies on the joint multidisciplinary expertise from biologists, physicists, chemists, clinicians and computer scientists, and depends on coordinated activities and knowledge transfer between academia and industry, and instrument developers and users. We have been actively participating in different activities organised within this rapidly growing network. Sladoje is engaged in the COMULIS Core Group as a Leader of WG4 (Correlation Software). Both Sladoje and Lindblad are MC members. We have contributed to organisation of several COMULIS events (conference, special sessions, workshops) during 2019. See Figure 17.

Figure 17: COMULIS (Correlated Multimodal Imaging in Life Sciences) - COST Action 17121

16. Sysmic: Development and application of systems microscopy for cancer cell migration Nicolas Pielawski, Anindya Gupta, Carolina Wahlby¨ Partner: Staffan Stromplad¨ and Carsten Daub, Dept. of Biosciences and Nutrition, KI, and SciLifeLab, Ulf Landegren, Dept. of Immunology, Genetics and Pathology, UU and SciLifeLab, Pontus Nordenfelt Dept. of Clinical Sciences, LU, Olink Bioscience and Sprint Bioscience Funding: Swedish Foundation for Strategic Research (SSF) Period: 20180122– Abstract: The core biological theme of this project is cell migration; a basic but complex cellular process that is highly relevant to human cancer. This complexity is, in part, explained by plasticity in the possible cell migration strategies that cells adopt and by the fine-tuned spatiotemporal coordination of migratory forces. However, the molecular mechanisms and genetic regulation that give rise to cell migration plasticity and dynamic force control constitutes knowledge gaps that this project proposes to fill. We develop learning- based image analysis methods to identify different cell migration modes and traction force microscopy models; measurements that will later be combined with single-cell proteomics, and multiplex in situ protein detection. We will thus combine in a novel manner dynamic and quantitative microscopy observations of migrating cells with single cell RNA-seq and proteomics. All this is tailored to add to the understanding of cellular dynamics. Finally, we will explore developed migration and traction force models and profiling methods on 25 cancer cell lines. See Figure 18.

35 Figure 18: Sysmic: Development and application of systems microscopy for cancer cell migration

17. Efficient virus segmentation and classification in TEM images with minimal labeling Damian J. Matuszewski, Ida-Maria Sintorn Funding: SciLifeLab, Swedish Research Council Period: 20180101– Abstract: Convolutional neural networks (CNNs) offer human experts-like performance and in the same time they are faster and more consistent in their judgment. However, their successful training and use in many biomedical and clinical applications is often restricted by an insufficient amount of annotated images, the quality of the annotations and the cost of the state-of-the-art hardware required for analyzing the images. In this project, we develop methods for training efficient CNNs from images with minimal annotation. We also investigate the possibility of making CNNs lighter by parametrizing and modifying the popular U- Net architecture and decreasing the number of its trainable weights. We use a challenging application of a pixel-wise virus classification in Transmission Electron Microscopy images to demonstrate our methods. During 2018 a paper entitled “Minimal annotation training for segmentation of microscopy images” was presented at the IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) and published in the conference proceedings. https://doi.org/10.1109/ISBI.2018.8363599 See Figure 19.

Figure 19: Efﬁcient virus segmentation and classiﬁcation in TEM images with minimal labeling

36 5.3 Microscopy: model organisms and tissues 18. CerviScan Ewert Bengtsson, Joakim Lindblad Partner: R. Kumar, Devanand, RU Dweepak, Centre for Development of Advanced Computing (CDAC), Thiruvananthapuram, Kerala, India; K. Sujathan, Regional Cancer Centre, Thiruvananthapuram, Kerala Funding: VINNOVA; Swedish Research Council; SIDA Period: 20080101– Abstract: Cervical cancer kills annually over a quarter of a million women world-wide. Screening for signs of cancer precursors using the Pap-test could reduce this. However, this requires highly trained cytotech- nologists and is time consuming. In spite of 50 years attempts to automate this process no cost effective systems are widely used. The CerviScan project is an initiative from the Indian government, run by CDAC and RCC in Kerala and CBA in Sweden, aimed at creating a low cost, automated screening system. A prototype system was created and used to screen over 1000 specimen with promising classiﬁcation results but too slow for deployment. After a long wait for renewed funding in India a new phase of the project focusing on dedicated hardware and a more streamlined, high-throughput system was started in 2018. In Sweden the funding for our collaboration from the Swedish Research Links Programme has ended but we are still taking part in the project since it has strong synergy effects with our work on oral cancer. In May 2019 we had a two day workshop at CDAC in Thiruvananthapuram and presented our work at a public seminar on cell biology and machine learning in cancer research. See Figure 20.

Figure 20: CerviScan

19. TissUUmaps - Integrating spatial and genetic information via automated image analysis and interactive visualization of tissue data Carolina Wahlby,¨ Gabriele Partel, Leslie Solorzano, Axel Andersson Partner: Mats Nilsson, Markus Hilscher, Jessica Svedlund and Xiaoyan Qian - Stockholm University/SciLifeLab Funding: ERC consolidator grant to Carolina Wahlby¨ Period: 201109– Abstract: Digital imaging of tissue samples and genetic analysis by next generation sequencing are two rapidly emerging fields in pathology. Digital pathology will soon be as common as digital images in radiology, and genetic analysis is rapidly evolving thanks to the impressive development of next generation sequencing technologies. However, most of today’s available technologies result in a genetic analysis that is decoupled from the morphological and spatial information of the original tissue sample, while many important questions in tumor- and developmental biology require single cell spatial resolution to understand tissue heterogeneity. In this project, we develop computational methods that bridge these two emerging fields. We combine spatially resolved high-throughput genomics analysis of tissue sections with digital image analysis of tissue morphology. Together with collaborators from the biomedical field, we work with advanced digital image processing methods for spatially resolved genomics (Ke et al, Nature Methods 2013). The project has

37 been enriched by a visualization tool (available at https://tissuumaps.research.it.uu.se/), a transcriptome supervised model for tissue classiﬁcation, as well as a novel image analysis pipeline that combines a learning approach with graphical model to increase recall. See Figure 21.

Figure 21: TissUUmaps - Integrating spatial and genetic information via automated image analysis and interactive visualization of tissue data

20. SciLifeLab BioImage Informatics Facility Carolina Wahlby,¨ Petter Ranefall, Anna Klemm, Amin Allalou, Hanqing Zhang Partner: Kevin Smith, Gisele Miranda, KTH Funding: SciLifeLab BioImage Informatics Facility (www.scilifelab.se/facilities/bioimage-informatics) Period: 20170101– Abstract: The ScilifeLab BioImage Informatics Facility (BIIF) provides support and education in microscopy image image analysis to Swedish lifescience researchers. We do not primarily analyze data for our users, but rather help users get started with their own analysis. BIIF has two nodes; one in Stockholm, connected to the School of Computer Science and Communication at KTH, and one in Uppsala, at the Centre for Image Analysis, Dept. of Information Technology, UU. During 2019, we have been involved in more than 40 different support projects, ranging from a few hours to more long-term. E.g., the facility was offering support on: analyzing high-content-screening data using CellProfiler and KNIME; classification and counting of cells in tumor micro arrays (TMA) using QuPath and the segmentation of kinetochore during the cell division of yeast using Fiji. We have also been teaching in around 20 different courses and workshops, both in Sweden and mainly via the Network for European BioImage Analysts (NEUBIAS) in other Eu- ropean countries. Thanks to two SciLifeLab technology development projects, we have since 2018 been developing new techniques for high-throughput/high content imaging and analysis of zebrafish embryos, an important model organism in drug screening and large-scale studies of diseases (cardiovascular, neural disorders and cancer). See Figure 22.

21. Zebrafish Image Informatics (ZII) Amin Allalou, Hanqing Zhang Partner: Johan Ledin - Genome Engineering Zebrafish, SciLifeLab. Tatjana Haitina - Dept. of Organismal Biology, Evolution and Development, UU. Marcel den Hoed, Dept. of Immunology, Genetics and Pathol- ogy and SciLifeLab, UU Funding: SciLifeLab TDP Period: 20181201– Abstract: The BioImage Informatics Facility (BIIF) and the Genome Engineering Zebrafish (GEZ) have launched a joint capability for zebrafish experiments: Zebrafish Image Informatics (ZII). The ZII capability depend on GEZ capacity for high-throughput imaging of zebrafish larva and BIIF expertise in large scale analysis of imaging data. Current projects: High-throughput screening in live zebrafish, Quantitative gene expression screening of in situ stained zebrafish and Deep learning for Zebrafish segmentation See Figure 23.

38 Figure 22: SciLifeLab BioImage Informatics Facility

Figure 23: Zebraﬁsh Image Informatics (ZII)

22. Quantitative gene expression screening of in situ stained zebrafish Amin Allalou, Carolina Wahlby¨ Partner: Johan Ledin - Genome Engineering Zebrafish, SciLifeLab. Maria Tenje - Customized Microflu- idics, SciLifeLab. Funding: SciLifeLab TDP Period: 20180101– Abstract: Researchers using the Genome Engineering Zebrafish (GEZ) to generate mutant lines are often in need of subsequent phenotyping of the mutant line. This requires careful characterization of mutant lines using panels of well-established cell- and tissue-specific markers and whole mount in situ hybridization (WISH). However, many researchers are only left with in situ stained fish and no good tools or knowledge of how to extract unbiased statistical information regarding the gene expression. Results are often based on manual counting and region estimation from 2D projection images of a small number of samples. Each mutant line generated at the GEZ carries an abundance of information in their expression, using low throughput and visual quantification will miss the more subtle, yet important, phenotypes. In this project we will developed a state-of-the-art analysis pipeline consisting of a 3D imaging technique (Optical projection tomography) for chromogenic WISH stain and subsequent image analysis methods. The analysis will provide unbiased quantification and localization of expression patterns and detect statistical significant dif-

39 ferences in any mutant line. This project is a collaboration between BioImage Informatics facility, Genome Engineering Zebraﬁsh and Customized Microﬂuidics at SciLifeLab. See Figure 24.

Figure 24: Quantitative gene expression screening of in situ stained zebraﬁsh

23. TMA core analysis for protein co-expression studies Leslie Solorzano, Carolina Wahlby¨ Partner: Carla Pereira and Carla Oliveira, University of Porto, Portugal Funding: ERC: Ref 682810 to C.W Period: 20180301– Abstract: Immunohistochemical (IHC) analysis of tissue biopsies is currently used for clinical screening of solid cancers to assess biomarker staining. The large amount of image data produced from these tissue samples calls for specialized computational pathology methods to perform integrative analysis. Even though biomarkers are traditionally studied independently, it is recognized that the study of biomarker co- expression may offer new insights towards patients’ clinical and therapeutic decisions. To explore protein co-expression, we constructed a modular image analysis pipeline to spatially align tissue microarray (TMA) image slides, evaluate alignment quality, deﬁne tumor regions, and ultimately quantify protein expression, with and without tumor segmentation. The pipeline was built with open-source tools that can manage gi- gapixel slides. See Figure 25.

Figure 25: TMA core analysis for protein co-expression studies

24. High-throughput screening in live zebraﬁsh Amin Allalou, Hanqing Zhang Partner: Johan Ledin - Genome Engineering Zebraﬁsh, SciLifeLab. Tatjana Haitina - Dept. of Organismal Biology, Evolution and Development, UU. Funding: SciLifeLab TDP Period: 20181201– Abstract: In this project we aim to develop and implement a high-throughput phenotypic screening platform

40 capable of functionally screening thousands of human disease-associated gene variants in vivo. By developing novel computation tools for automated image analysis and combining them with high-throughput 3D fluorescence imaging of live zebrafish using the VAST (Vertebrate Automated Screening Technology) system, we will quantify gene expression and extract morphological features with high precision. We will be able to detect subtle features that can often not be detected and statistically validated by visual inspection of a small number of samples. In addition, by extracting as much information as possible per fish and experiment we can learn and understand multiple effects and hopefully improve the starting point for experiments done on more advanced animals and clinical trials. By implementing this new technology and providing state-of-the art 3D imaging and quantitative analysis as a joint GEZ (Genome Engineering Zebrafish) - BIIF (BioImage Informatics facility) SciLifeLab service we will be able to offer users an analysis currently not available anywhere else. See Figure 26.

Figure 26: High-throughput screening in live zebraﬁsh

41 5.4 Mathematical and geometrical theory 25. Digital Distance Functions and Distance Transforms Robin Strand, Gunilla Borgefors Partner: Benedek Nagy - Dept. of Computer Science, Faculty of Informatics, University of Debrecen, Hun- gary; Nicols Normand, IRCCyN - University of Nantes, France Funding: TN-faculty, UU Period: 19930901– Abstract: The distance between any two grid points in a grid is defined by a distance function. In this project, weighted distances have been considered for many years. A generalization of the weighted distances is obtained by using both weights and a neighborhood sequence to define the distance function. The neighborhood sequence allows the size of the neighborhood to vary along the paths. A manuscript on distance functions based on multiple types of weighted steps combined with neighborhood sequences has been produced in collaboration with Strand, Nagy and Normand. The manuscript holds (mainly theoretical) results on for example metricity and parameter optimization. The figure illustrates the shapes of disks with different number of weights, when the optimization criterion is roundness in the Euclidean sense. See Figure 27.

Figure 27: Digital Distance Functions and Distance Transforms

26. Convexity of marginal functions in the discrete case Christer Oscar Kiselman Partner: Shiva Samieinia, Intercard Period: 20100111– Abstract: We deﬁne, using difference operators, classes of functions deﬁned on the set of points with integer coordinates which are preserved under the formation of marginal functions. The duality between classes of functions with certain convexity properties and families of second-order difference operators plays an important role and is explained using notions from mathematical morphology. 27. Discrete convolution equations Christer Oscar Kiselman Partner: Jan Boman, Dept. of Mathematics, Stockholm University, Ragnar Sigurdsson, University of Ice- land Period: 20120111– Abstract: We study solvability of convolution equations for functions with discrete support in mathbfRn, a special case being functions with support in the integer points. The more general case is of interest for several grids in Euclidean space, like the body-centred and face-centered tesselations of three-space, as well as for the non-periodic grids that appear in the study of quasicrystals. The theorem of existence of fundamental solutions by de Boor, Hollig¨ & Riemenschneider is generalized to general discrete supports, using only elementary methods. We also study the asymptotic growth of sequences and arrays using the Fenchel transformation. Estimates using the Fourier transformation have been studied. Now duality of convolution will be investigated. A study of quasicrystals is part of this project. 28. Coverage Model and its Application to High Precision Medical Image Processing Natasaˇ Sladoje, Joakim Lindblad Partner: Buda Bajic,´ Slobodan Draziˇ c,´ Faculty of Technical Sciences, University of Novi Sad, Serbia Funding: VINNOVA; TN-faculty, UU; Swedish Research Council Period: 201409—- Abstract: The coverage model, which we have been developing for several years, provides a framework for representing objects in digital images as spatial fuzzy sets. Membership values indicate to what extent

42 image elements are covered by the imaged components. The model is useful for improving information extraction from digital images and reducing problems originating from limited spatial resolution. We have by now developed methods for estimation of a number of features of coverage representation of shapes and demonstrated their increased precision and accuracy, compared to the crisp representations. In 2019 our focus has been on the development of segmentation methods which result in coverage segmentation. We have published (in Journal of Electronic Imaging, Vol 28) a coverage segmentation method based on energy minimization, which improves and generalizes our previously published results. The improved method is applicable to blurred and noisy images, and provides coverage segmentation at increased spatial resolution, while preserving thin fuzzy object boundaries. We have suggested a suitable global optimization scheme to address the challenging non-convex optimization problem. We have evaluated the method on synthetic and real images, confirming its very good performance. Both Buda and Slobodan have defended their PhD theses in 2019, in February and September, respectively. 29. Regional Orthogonal Moments for Texture Analysis Ida-Maria Sintorn Partner: Vironova AB; Sven Nelander, Dept. of Immunology, Genetics and Pathology, UU Funding: Swedish Research Council Period: 201501—- Abstract: The purpose of this project is to investigate and systematically characterize a novel approach for texture analysis, which we have termed Regional Orthogonal Moments (ROMs). The idea is to combine the descriptive strength and compact information representation of orthogonal moments with the well- established local filtering approach for texture analysis. We will explore ROMs and quantitative texture descriptors derived from the ROM filter responses, and characterize them with special consideration to noise, rotation, contrast, scale robustness, and generalization performance, important factors in applications with natural images. In order to do this we will utilize and expand available image texture datasets and adapt machine learning methods for microscopy image prerequisites. The two main applications for which we will validate the ROM texture analysis framework are viral pathogen detection and identification in MiniTEM images, and glioblastoma phenotyping of patient specific cancer stem cell cultures for disease modeling and personalized treatment. During 2016, a paper comparing and evaluating several ROM filter banks on a number of different texture datasets was submitted and is awaiting the review response. 30. Precise Image-Based Measurements through Irregular Sampling Teo Asplund, Robin Strand, Gunilla Borgefors Partner: Cris L. Luengo Hendriks-Flagship Biosciences Inc., Westminster, Colorado, USA, Matthew J. Thurley-Lulea˚ University of Technology, Sweden Funding: Swedish Research Council Period: 20150401– Abstract: We develop mathematical morphology on irregularly sampled signals. This is beneficial for a number of reasons: 1. Irregularly sampled signals would traditionally have to be resampled onto the regular grid to allow morphology to be applied. However, such resampling can require interpolating data where the original signal contained large holes. This can lead to very poor performance. 2. The morphological operators depend on suprema/infima in the signal. A regularly sampled signal is likely to miss these. 3. The operators produce lines along which the derivative is not continuous, thereby introducing unbounded frequencies and breaking the correspondence between the sampled signal and the continuous bandlimited one. 4. The structuring element is limited by the sampling grid. We have shown that moving to morphology on irregularly sampled signals can yield results that better approximate continuous morphology, on regularly sampled signals, than the traditional morphological operators, yielding more accurate measurements both in 1D- and 2D grayscale morphology. The framework has also been generalized to allow for adaptive morphology. We have applied the developed methods to irregularly sampled data, such as 3D point clouds, recently in order to segment point clouds of urban scenes. In December of 2019, a PhD-thesis connected to this project was completed. See Figure 28.

43 Figure 28: Precise Image-Based Measurements through Irregular Sampling

31. Distance Measures Between Images Based on Spatial and Intensity Information, with Applications in Biomedical Image Processing Johan Ofverstedt,¨ Natasaˇ Sladoje, Joakim Lindblad Partner: Ida-Maria Sintorn, Vironova AB Funding: VINNOVA, TN-faculty Period: 20170101– Abstract: Many fundamental image analysis tasks such as image registration, template matching, and image retrieval, can be solved successfully by methods utilizing notions of distance (or similarity) between images. Within this project, we study distances combining intensity and efficiently encoded spatial information, methods for computing them efficiently, and suitable applications where they can lead to robust and accurate solutions. Currently the focus of the project is to develop deformable image registration methods based on these distances, and to develop robust general purpose multi-modal image registration methods. Recent outcomes include the article ”Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information” in IEEE TIP, 2019, and the conference publication ”Stochas- tic Distance Transform” orally presented at DGCI, 2019. Two non-reviewed papers, ”Robust Symmetric Affine Image Registration”, and ”Stochastic Distance Functions with Applications in Object Detection and Image Segmentation”, were orally presented at SSBA2019, in Goteborg.¨ See Figure 29.

Figure 29: Distance Measures Between Images Based on Spatial and Intensity Information, with Appli- cations in Biomedical Image Processing

44 32. Robust learning of geometric equivariances Karl Bengtsson Bernander, Natasaˇ Sladoje, Joakim Lindblad Funding: WASP (Wallenberg AI, Autonomous Systems and Software Program) Period: 20180903– Abstract: The proposed project builds on, and extends work on Geometric deep learning and aims at combining it with Manifold learning, to produce truly learned equivariances without the need for engineered solutions and maximize benefits of shared weights. A decrease of the number of parameters to learn leads to increased performance, generalizability and robustness of the network. An additional gain is in reducing a risk that the augmented data incorporates artefacts not present it the original data. A typical example is textured data, where interpolation performed in augmentation by rotation and scaling unavoidably affects the original texture and may lead to non-reliable results. Reliable texture-based classification is, in many cases, of high importance in biomedical applications. This project is conducted within AI-Math track of WASP the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program, a Swedish national initiative for strategically motivated basic research, education and faculty recruitment. In 2019 we extended the literature study and worked on reproducing the existing methods. We presented a poster at the 4th WASP winter conference in Linkoping,¨ at the Max Planck Institutes for Intelligent Systems in Stuttgart and Tubingen,¨ and at the universities of Darmstadt and Aachen as part of the WASP-AI study trip. See Figure 30.

Figure 30: Robust learning of geometric equivariances

33. Efficient isosurface rendering for visualization Fredrik Nysjo,¨ Filip Malmberg, Ingela Nystrom¨ Funding: TN faculty Period: 20180101– Abstract: Real-time rendering of isosurfaces in large volume datasets can be a challenge, especially for virtual reality applications that require low latency and high update rates. In this project, we develop an efficient hybrid rendering method, RayCaching, that combines rasterisation and raycasting to amortise the cost of rendering a volume over several frames. This work was published in Computer Graphics Forum in 2019. We further develop a memory efficient data structure that allows both rasterisation and ray tracing of isosurfaces extracted from large volume data. This work is now also submitted for publication. See Figure 31. 34. Max-norm optimization in image analysis and computer vision Filip Malmberg, Robin Strand Partner: Krzysztof C. Ciesielski, Dept. of Mathematics, West Virginia University, Morgantown, WV, USA; Dept. of Radiology, MIPG, University of Pennsylvania, PA, USA; Funding: TN faculty Period: 20190101– Abstract: Many fundamental problems in image processing and computer vision, such as image filtering, segmentation, registration, and stereo vision, can naturally be formulated as optimization problems. Solving

45 Figure 31: Efﬁcient isosurface rendering for visualization

these optimization problems is often difficult, and generally involves minimizing a non-convex function depending on thousands of variables. Combinatorial, discrete optimization techniques have proven to be very successful in solving optimization problems in many image processing applications. The aim of this project is to study a specific class of optimization problems, where the objective function is defined as the max-norm, or L -norm, over a set of variables. Such optimization problems have occurred frequently in 1 the image processing literature. It is well known that for many specific max-norm optimization problems, globally optimal solutions can be found using very efficient quasi-linear time algorithms. Notably, the ubiquitous watershed segmentation method can be shown to produce a globally optimal solution to a max- norm optimization problem. Despite the success of these methods in solving certain special cases, their limits in terms of general max-norm optimization remains unclear. The overall aim of this project is to provide a detailed characterization of the class of max-norm problems that can be solved using efficient, low-order polynomial time algorithms. See Figure 32.

Figure 32: Max-norm optimization in image analysis and computer vision

35. Graph neural networks and their application in imaging Teo Asplund, Eva Breznik Funding: department Period: 201909– Abstract: We investigate graph neural networks for image segmentation, where the aim is to evaluate the role and effectiveness of nonlinearities at various stages. We intend to start with a simplified formulation of a graph convolutional neural network from Wu et. al.: Simplifying Graph Convolutional Networks, which only contains one nonlinearity layer. The simplified network can then be gradually extended with additional nonlinearities of various types. Among them, potentially interesting ones would be operations inspired by mathematical morphology, since they are related to the commonly used max/min filters and could prove beneficial for image data. See Figure 33.

46 Figure 33: Graph neural networks and their application in imaging

5.5 Humanities 36. Image Analysis for Landscape Analysis Anders Brun Partner: Bo Malmberg, Michael Nielsen, Dept. of Human Geography, Stockholm University; Anders Wastfelt,¨ Dept. of Economics, SLU Funding: SLU; Stockholm University Period: 200901—- Abstract: This project is a collaboration with researchers at SU and SLU. It aims to derive information about rural and city landscapes from satellite images. The project focuses on using texture analysis of images, rather than only pixelwise spectral analysis, to segment the image into different meaningful regions. This is an ongoing collaboration, which has so far resulted in one patent and one journal publication on the detection of damaged forest from aerial photographies. See Figure 34.

Figure 34: Image Analysis for Landscape Analysis

37. Recognition and Datamining for Handwritten Text Collections Anders Brun, Ewert Bengtsson, Fredrik Wahlberg, Tomas Wilkinson, Kalyan Ram, Anders Hast, Ekta Vats Partner: Carl Nettelblad, Dept. of Information Technology, Lasse Martensson, Dept. of Business and Economics Studies, Hogskolan¨ i Gavle;¨ Mats Dahllof,¨ Dept. of Linguistics and Philology, UU; Alicia Fornes,´ Universitat Autonoma de Barcelona, Spain; Jonas Lindstrom,¨ Dept. of History, UU Funding: UU; Swedish Research Council; Riksbankens Jubileumsfond; eSSENCE Period: 20120101– Abstract: This cross disciplinary initiative takes its point of departure in the analysis of handwritten text manuscripts using computational methods from image analysis and linguistics. It sets out to develop a manuscript analysis technology providing automatic tools for large-scale transcription, linguistic analysis,

47 digital paleography and generic data mining of historical manuscripts. The mission is to develop technology that will push the digital horizon back in time, by enabling digital analysis of handwritten historical materials for both researchers and the public. One postdoc started and several new results were presented. See Figure 35.

Figure 35: Recognition and Datamining for Handwritten Text Collections

38. Writer Identiﬁcation and Dating Anders Brun, Fredrik Wahlberg, Anders Hast, Ekta Vats Partner: Lasse Martensson, Dept. of Business and Economics Studies, Hogskolan¨ i Gavle;¨ Mats Dahllof,¨ Dept. of Linguistics and Philology, UU; Alicia Fornes,´ Universitat Autonoma de Barcelona, Spain Funding: UU; Swedish Research Council; Riksbankens Jubileumsfond; eSSENCE Period: 201401—- Abstract: The problem of identifying the writer of some handwritten text is of great interest in both forensic and historical research. Sadly the magical CSI machine for identifying a scribal hand does not exist. Using image analysis, statistical models of how a scribe used the quill pen on a parchment can be collected. These measurements are treated as a statistical distribution over writing practices. We are using this information to identify single writers and perform style based dating of historical manuscripts. During 2016 we continuted to analyze over 10000 manuscript pages form the collection Svenskt Diplomatarium, from Riksarkivet. Using our newest methods, based on recent trends in deep learning, we are able to estimate the production date of a manuscript in this collection with a median error of less than 12 years. See Figure 36.

39. Historical Handwritten Text Recognition Ekta Vats, Anders Hast Partner: Per Cullhed - University Library, UU, Lasse Martensson˚ - Dept. of Swedish Language and Mul- tilingualism, Stockholm University, Alicia Fornes´ - Universitat Autonoma de Barcelona, Spain, Prashant Singh - Dept. of Information Technology, UU Funding: Swedish e-Science Academy (eSSENCE) Period: 20170501– Abstract: Automatic recognition of poorly degraded handwritten text is challenging due to complex lay- outs and paper degradations over time. Typically, an old manuscript suffers from degradations such as paper stains, faded ink and ink bleed-through. There is variability in writing style, and the presence of text and symbols written in an unknown language. This hampers the document readability, and renders the task of transcription and word spotting in a set of non-indexed documents, to be more difﬁcult. The aim of this project is to facilitate basic research on handwritten text recognition by developing efﬁcient methods for

48 Figure 36: Writer Identiﬁcation and Dating

recognition of complex handwritten text using advanced HTR technology. The present investigation be- longs to a set of methods known as word spotting, that accelerate the word recognition process by finding multiple instances of a word on-the-fly in a set of unedited material. PI Anders Hast, along with postdoc Ekta Vats, have achieved significant advances in HTR research with scientific peer-reviewed publications that are highly relevant to this project. See Figure 37.

Figure 37: Historical Handwritten Text Recognition

40. Computerised Image Processing in Handwritten Text Recognition Raphaela Heil, Anders Hast, Ekta Vats, Anders Brun Partner: Lasse Martensson,˚ Dept. of Scandinavian Languages, Uppsala University Funding: TN-Faculty Period: 20180115– Abstract: This project is concerned with handwritten text recognition with a special focus on the handling of historical documents. It entails the development and implementation of new computational methods for the recognition, transcription and analysis of manuscripts, employing both learning-free and learning-based approaches. In addition to this, a set user-friendly tools for the transcription and analysis of historical documents will be implemented, encompassing the previously developed methods, thus making them accessible to researchers from the digital humanities. During the academic year of 2019, a prototype for a ﬁrst tool was developed. IASC, the Interactive Atlas of Script Characteristics, visualizes the variation in the features of words or single characters in an interactive 2D environment, enabling palaeographers to explore the

49 different writing styles contained in a manuscript. See Figure 38.

Figure 38: Computerised Image Processing in Handwritten Text Recognition

41. Speaking to one’s superiors: Petitions as cultural heritage and sources of knowledge Anders Brun, Anders Hast Partner: History department, the Dept. of Linguistics and Philology, and the Dept. of Information technology. The archival unit is Landsarkivet Uppsala. Funding: Swedish Research Council Period: 20190101– Abstract: The purpose of this project is to enhance accessibility to and knowledge of a historical source petitions used relatively little in Sweden, and to use this source to answer questions about people’s ways of supporting themselves and claiming rights in the past. Petitions have their name from the Latin verb peto: go to, beseech, entreat. The right to petition is vital to our understanding of pre-modern political life. While not democratic in the modern sense of the word, communities and societies that acknowledged people’s right to present petitions cannot be described as despotic or absolutist either. This project will (a) Photo- graph and make publicly accessible a large volume of handwritten eighteenth-century petitions (Swedish supplik) (b) Make high-quality indexes to the petitions (c) Strategically select a number of petitions for refined analysis of form, language, content and context (d) Contribute to the history of everyday politics and micro-level economy (e) Contribute to digital philology and the development of automatized historical text analysis The project creates research infrastructure, combines insights and skills from several scholarly and scientific fields, and is an example of collaboration between university and archives as well as an example of digital humanities. See Figure 39.

Figure 39: Speaking to one’s superiors: Petitions as cultural heritage and sources of knowledge

50 5.6 Cooperation partners

6 Partners CBA

0 1 234 5678910 11 12 13 14 15 16

Figure 40: This diagram illustrates the cooperations we have when writing reviewed articles. On the horizontal axes the number of authors are given, and on the vertical the number of papers with that number of authors. In blue is the average number of authors from CBA and in orange the number of co-author partners. Thus there were nine papers with four authors, of which two were from CBA, on the average.

International Dept. of Pediatrics, and Obesity Research Unit, Paracelsus Medical University, Salzburg, Austria Dept. of Radiology, Paracelsus Medical University, Salzburg, Austria Dept. of Pediatrics, Medical University of Vienna, Austria Dept. of Obstetrics & Gynecology, University of Manitoba, Winnipeg, Canada Dept. of Medical Microbiology, University of Manitoba, Winnipeg, Canada Systems and Computing Engineering Department, Universidad de los Andes, Bogota,´ Colombia University of Southern Denmark, Odense, Denmark Thomas Johann Seebeck Dept. of Electronics, Tallinn University of Technology, Estonia Eliko Tehnoloogia Arenduskeskus OU¨ , Tallinn, Estonia Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Finland Faculty of Medicine and Life Sciences, University of Tampere, Finland Universite´ Clermont Auvergne, Institut Pascal, Clermont-Ferrand, France MINES ParisTech, PSL Research University, CMM - Centre for Mathematical Morphology, Fontainebleau, France Centre Hospitalier Emile´ Roux, Le Puy-en-Velay, France Service de Reanimation´ Medicale,´ Hospices Civils de Lyon, Hopitalˆ de la Croix Rousse, Lyon, France

51 Assistance Publique Hopitauxˆ de Paris, Hopitalˆ Necker Enfants-Malades, Service de Chirurgie Maxillofaciale et Plastique, Universite´ Paris Descartes, Paris, France CREATIS UMR, Universite´ Claude Bernard Lyon, Villeurbanne, France IRCCyN, University of Nantes, France Research Group Development and Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany Biomedical Center and Walter-Brendel-Zentrum fur¨ Experimentelle Medizin, Ludwig-Maximilians-Univeristat¨ Munich, Germany Division of Evolutionary Biology, Faculty of Biology, LMU Munich, Germany Centre for Development of Advanced Computing (CDAC), Thiruvananthapuram, Kerala, India Dept. of Computer Science, Faculty of Informatics, University of Debrecen, Hungary Dept. of Biotechnology, Maulana Abul Kalam Azad University of Technology (MAKAUT), West Bengal, India Dept.of Electrical Engineering, National Institute of Technology Durgapur, West Bengal, India Dept. of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, India Dept. of Radiodiagnosis and Imaging, Post-graduate Institute of Medical Education and Research, Chandigarh, India Regional Cancer Centre, Thiruvananthapuram, Kerala, India RCC Institute of Information Technology, Kolkata, India Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India Faculty of Computer Science, Brawijaya University, Malang, Indonesia Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran Institute of Informatics and Telematics, CNR, Pisa, Italy Institute of Artiﬁcial Intelligence and Cognitive Engineering, University of Groningen, The Netherlands Dept. of Arctic Geology, UNIS, The University Center in Svalbard, Norway Dept. of Information Technology, Østfold University College, Norway Faculty of Medicine, University of Porto, Portugal Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, Romania Dept. of Pathology, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania Mathematical Institute of the Serbian Academy of Sciences and Arts, Belgrade, Serbia Faculty of Technical Sciences, University of Novi Sad, Serbia Universitat Autonoma de Barcelona, Spain Eastern Mediterranean University, North Cyprus, Turkey Dept. of Chemical Engineering and Biotechnology, Cambridge University, UK Dept. of Ophthalmology, Cambridge University Hospitals NHS Trust and Vision and Eye Research Institute (VERI), Anglia Ruskin University, Cambridge, UK Wolfson College, University of Cambridge, UK Institute of Geography, School of GeoSciences, University of Edinburgh, UK School of Geography and Geosciences, University St Andrews, UK Craniofacial Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK National Centre for HIV/AIDS, Viral Hepatitis, Sexually Transmitted Disease and Tuberculosis Prevention, CDC, Atlanta, USA

52 Bradley Dept. of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacks- burg, USA Broad Institute of Harvard and MIT, Boston, USA Dept. of Ophthalmology, Harvard Medical School, Boston Computer Science and Engineering, University at Buffalo, USA Dept. of Electrical and Computer Engineering, University of Iowa, USA Dept. of Radiology, University of Iowa, USA Center for Predictive Medicine, University of Louisville, USA Dept. of Pharmacology and Toxicology, University of Louisville, USA James Graham Brown Cancer Center, University of Louisville, USA Mt Sinai-Beth Israel Hospital, New York, USA Dept. of Radiology, MIPG, University of Pennsylvania, USA Magee-Womens Research Institute, Pittsburgh, USA School of Pharmacy, University of Pittsburgh, USA Dept. of Molecular Biology and Princeton Neuroscience Institute, Princeton University, Princeton, USA Dept. of Biochemistry and Molecular Medicine, University of California Davis School of Medicine, Sacramento, USA Dept. of Mathematics, West Virginia University, USA Terra3D, Paris, France

National Dept. of Electrical Engineering, Chalmers University of Technology, Goteborg¨ Dept. of Clinical Neuroscience, Sahlgrenska Academy, University of Gothenburg, Goteborg¨ Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Goteborg¨ Dept. of Business and Economics Studies, University of Gavle¨ Dept. of Industrial Development, IT and Land Management, University of Gavle¨ Division of GIScience, Faculty of Engineering and Sustainable Development, University of Gavle¨ Centre for Medical Image Science and Visualization, Linkoping¨ University Dept. of Clinical and Experimental Medicine, Linkoping¨ University Dept. of Physics, Chemistry and Biology (IFM), Linkoping¨ University Lulea˚ University of Technology Dept. of Clinical Sciences, Lund University, Lund Dept. of Biosciences and Nutrition, Karolinska Institute, Stockholm Dept. of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm Dept. of Pathology and Cytology, Karolinska Institute, Stockholm SciLifeLab, Stockholm Dept. of Biochemistry and Biophysics, Stockholm University Dept. of Swedish Language and Multilingualism, Stockholm University Dept. of Paediatrics, Central Hospital, Vaster¨ as˚ Antaros Medical AB, BioVenture Hub, Molndal¨ AstraZeneca AB, Stockholm

53 Folktandvarden˚ AB, Stockholm Public Dental Service, Sodersjukhuset,¨ Stockholm Vironova AB, Stockholm

Local Children Obesity Clinic, UU Hospital Dept. of Earth Sciences, UU Dept. of History, UU Dept. of Immunology, Genetics and Pathology, UU Dept. of Linguistics and Philology, UU Dept. of Medical Cell Biology, UU Dept. of Medical Sciences, UU Dept. of Neuroscience, UU Dept. of Organismal Biology, UU Dept. of Pharmaceutical Biosciences, UU Dept. of Scandinavian Languages, UU Dept. of Surgical Sciences, UU SciLifeLab, UU University Library, UU Dept. of Women’s and Children’s Health, UU Dept. of Economics, SLU, Uppsala Astrego Diagnostics AB, Uppsala CADESS Medical AB, Uppsala Olink Bioscience, Uppsala Sprint Bioscience, Uppsala

54 6 Publications In 2019, we published 48 articles in scientific journals or fully reviewed proceedings, see Fig- ure 41. We also had a number of papers in abstract reviewed proceedings and a few other ones. Of the 34 journal papers only four described new image analysis theory, while the rest described research in various applications. However, among the 14 proceedings papers the majority, eight, were theoretical. This mostly reflects different publications patterns between us and our collaboration partners in different application areas. The articles were almost all published in different journals and proceedings, reflecting the diverse needs of our fundamental research subjects. It should be noted that in our field, conference proceedings often have high importance and high rejection rates compared to some of the journals. Authors affiliated with CBA are in bold; they may also have other affiliations. As a curiousity, we have collected the abstracts of this year’s reviewed conference proceedings and made a so called word-cloud from them, to see which words emerge as most used, see Figure 42. A modern concept like deep learning is quite prominent in the word-cloud, while the words from the topic medical image processing are not presented. One explanation could be that our medical research is published in journals.

25 Proceedings Journal

0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Figure 41: The number of fully reviewed articles from CBA 2001–2019.

55 Figure 42: Word-cloud of abstracts of all reviewed conference proceedings that were published in 2019

6.1 Edited conference proceedings 1. Proceedings from 21st Scandinavian Conference on Image Analysis Book: Lecture Notes in Computer Science, Vol. 11482 Editors: Michael Felsberg, Per-Erik Forssen,´ Ida-Maria Sintorn, Jonas Unger Publisher: Springer Abstract: This volume constitutes the refereed proceedings of the 21st Scandinavian Conference on Image Analysis, SCIA 2019, held in Norrkoping,¨ Sweden, in June 2019. The 40 revised papers presented were carefully reviewed and selected from 63 submissions. The contribu- tions are structured in topical sections on Deep convolutional neural networks; Feature extraction and image analysis; Matching, tracking and geometry; and Medical and biomedical image analysis. 2. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications Book: Lecture Notes in Computer Science, Vol. 11896 Editors: Ingela Nystrom¨ , Yanio Hernandez Heredia(1), Vladimir Milian´ Nu`nez(1)˜ (1) University of Informatics Sciences, Havana, Cuba Publisher: Springer Verlag Abstract: This volume constitutes the refereed proceedings of the 24th Iberoamer- ican Congress on Pattern Recognition (CIARP 2019), held in Havana, Cuba, in October 2019.

6.2 Journal articles 1. Sparsity promoting super-resolution coverage segmentation by linear unmixing in presence of blur and noise Authors: Buda Bajic(1),´ Joakim Lindblad, Natasaˇ Sladoje (1) Faculty of Technical Sciences, University of Novi Sad, Serbia Journal: Journal of Electronic Imaging (SPIE), Vol. 28, No. 1, 013046 DOI: 10.1117/1.JEI.28.1.013046 Abstract: We present a segmentation method that estimates the relative coverage of each pixel in a sensed image by each image component. The proposed super-resolution blur-aware model (utilizes a priori knowledge of the image blur) for linear unmixing of image intensities relies on a sparsity promoting approach expressed by two main requirements: (i) minimization of Huberized total variation, providing smooth object boundaries and noise removal, and (ii) minimization of nonedge image fuzziness, responding to an assumption that imaged objects are crisp and that fuzziness is mainly due to the imaging and digitization process. Edge fuzziness due to partial coverage is allowed, enabling subpixel precise feature estimates. The segmentation is formulated as an energy minimization problem and solved by the spectral projected gradient method, utilizing a graduated nonconvexity scheme. Quantitative and qualitative evaluation on synthetic

56 and real multichannel images confirms good performance, particularly relevant when subpixel precision in segmentation and subsequent analysis is a requirement. 2. Deep Learning in Image Cytometry: A Review Authors: Anindya Gupta, Philip J. Harrison(1), Hakan˚ Wieslander, Nicolas Pielawski, Kimmo Kar- tasalo(2,3), Gabriele Partel, Leslie Solorzano, Amit Suveer, Anna H. Klemm(4), Ola Spjuth(1), Ida- Maria Sintorn, Carolina Wahlby¨ (4) (1) Dept. of Pharmaceutical Biosciences, UU (2) Faculty of Medicine and Life Sciences, University of Tampere, Finland (3) Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Finland (4) BioImage Informatics Facility, SciLifeLab, UU Journal: Cytometry, Vol. 95, No. 4, pp. 366–380 DOI: 10.1002/cyto.a.23701 Abstract: Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Start- ing with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. 3. RayCaching: Amortized Isosurface Rendering for Virtual Reality Authors: Fredrik Nysjo,¨ Filip Malmberg, Ingela Nystrom¨ Journal: Computer Graphics Forum, Vol. 39, No. 1, pp. 220–230 DOI: 10.1111/cgf.13762 Abstract: Real-time virtual reality requires efficient rendering methods to deal with high- resolution stereoscopic displays and low latency head-tracking. Our proposed RayCaching method renders isosurfaces of large volume datasets by amortizing raycasting over several frames and caching primary rays as small bricks that can be efficiently rasterized. An occupancy map in form of a clipmap provides level of detail and en- sures that only bricks corresponding to visible points on the isosurface are being cached and rendered. Hard shadows and ambient occlusion from secondary rays are also accumulated and stored in the cache. Our method supports real-time isosurface rendering with dynamic isovalue and allows stereoscopic visualization and exploration of large volume datasets at framerates suitable for virtual reality applications. 4. Conventional analysis of movement on non-flat surfaces like the plasma membrane makes Brownian motion appear anomalous Authors: Jeremy Adler(1), Ida-Maria Sintorn, Robin Strand, Ingela Parmryd(1,2) (1) SciLifeLab, Medical Cell Biology, UU (2) Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Sweden Journal: Communications Biology, Vol. 2, Article number: 12 DOI: 10.1038/s42003-018-0240-2 Abstract: Cells are neither flat nor smooth, which has serious implications for prevailing plasma membrane models and cellular processes like cell signalling, adhesion and molecular clustering. Using probability distributions from diffusion simulations, we demonstrate that 2D and 3D Euclidean distance measurements substantially underestimate diffusion on non-flat surfaces. Intuitively, the shortest within surface distance (SWSD), the geodesic distance, should reduce this problem. The SWSD is accurate for foldable surfaces but, although it outperforms 2D and 3D Euclidean measurements, it still underestimates movement on de- formed surfaces. We demonstrate that the reason behind the underestimation is that topographical features themselves can produce both super- and subdiffusion, i.e. the appearance of anomalous diffusion. Differen- tiating between topography-induced and genuine anomalous diffusion requires characterising the surface by simulating Brownian motion on high-resolution cell surface images and a comparison with the experimental data. 5. Developing adaptive traffic signal control by actor–critic and direct exploration methods Authors: Mohammad Aslani(1), Mohammad Saadi Mesgari(2), Stefan Seipel(1), Marco Wiering(3)

57 (1) Dept. of Industrial Development, IT and Land Management, University of Gavle,¨ Sweden (2) Dept. of Geospatial Information System (GIS), Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, Iran (3) Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen, the Netherlands Journal: Proceedings of the Institution of Civil Engineers - Transport, Vol. 172 Issue 5, pp. 289–298 DOI: 10.1680/jtran.17.00085 Abstract: Designing efficient traffic signal controllers has always been an important concern in traffic engineering. This is owing to the complex and uncertain nature of traffic environments. Within such a context, reinforcement learning has been one of the most successful methods owing to its adaptability and its online learning ability. Reinforcement learning provides traffic signals with the ability automatically to determine the ideal behaviour for achieving their objective (alleviating traffic congestion). In fact, traffic signals based on reinforcement learning are able to learn and react flexibly to different traffic situations without the need of a predefined model of the environment. In this research, the actor–critic method is used for adaptive traffic signal control (ATSC-AC). Actor–critic has the advantages of both actor-only and critic-only methods. One of the most important issues in reinforcement learning is the trade-off between exploration of the traffic environment and exploitation of the knowledge already obtained. In order to tackle this challenge, two direct exploration methods are adapted to traffic signal control and compared with two indirect exploration methods. The results reveal that ATSC-ACs based on direct exploration methods have the best performance and they consistently outperform a fixed-time controller, reducing average travel time by 21%. 6. Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology Authors: Christophe Avenel(1), Anna Tolf(2), Anca Dragomir(2,3), Ingrid B. Carlbom(1) (1) CADESS Medical AB, Uppsala, Sweden (2) Dept. of Pathology, Uppsala University Hospital (3) Dept. of Immunology, Genetics and Pathology, UU Journal: Frontiers in Bioengineering and Biotechnology, Vol. 7, article-id 125 DOI: 10.3389/fbioe.2019.00125 Abstract: Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists’ workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisition to keep color and intensity variations to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions. Since malignancy grading of prostate tissue according to Gleason or to the International Society of Uro- logical Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differ- entiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that can successfully determine the glandular boundaries. Accu- rate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands. 7. PDNet: Semantic segmentation integrated with a primal-dual network for document binarization Authors: Kalyan Ram Ayyalasomayajula, Filip Malmberg, Anders Brun Journal: Pattern Recognition Letters, Vol. 121, pp. 52–60 DOI: 10.1016/j.patrec.2018.05.011 Abstract: Binarization of digital documents is the task of classifying each pixel in an image of the document as belonging to the background (parchment/paper) or foreground (text/ink). Historical documents are often subjected to degradations, that make the task challenging. In the current work a deep neural network architecture is proposed that combines a fully convolutional network with an unrolled primal-dual network that can be trained end-to-end to achieve state of the art binarization on four out of seven datasets. Docu-

58 ment binarization is formulated as an energy minimization problem. A fully convolutional neural network is trained for semantic segmentation of pixels that provides labeling cost associated with each pixel. This cost estimate is refined along the edges to compensate for any over or under estimation of the foreground class using a primal-dual approach. We provide necessary overview on proximal operator that facilitates theoretical underpinning required to train a primal-dual network using a gradient descent algorithm. Numer- ical instabilities encountered due to the recurrent nature of primal-dual approach are handled. We provide experimental results on document binarization competition dataset along with network changes and hyper- parameter tuning required for stability and performance of the network. The network when pre-trained on synthetic dataset performs better as per the competition metrics. 8. TAF1, associated with intellectual disability in humans, is essential for embryogenesis and regulates neurodevelopmental processes in zebrafish Authors: Sanna Gudmundsson(1), Maria Wilbe(1), Beata Filipek-Gorniok(2),´ Anna-Maja Molin(1), Sara Ekvall(1), Josefin Johansson(1), Amin Allalou, Hans Gylje(3), Vera M. Kalscheuer(4), Johan Ledin(2), Goran¨ Anneren(1),´ Marie-Louise Bondeson(1) (1) Dept. of Immunology, Genetics and Pathology and SciLifeLab, UU (2) Dept. of Organismal Biology, Genome Engineering Zebrafish and SciLifeLab, UU (3) Dept. of Paediatrics, Central Hospital, Vaster¨ as˚ (4) Research Group Development and Disease, Max Planck Institute for Molecular Genetics, Berlin, Ger- many Journal: Scientific Reports, Vol. 9, Article number: 10730 DOI: 10.1038/s41598-019-46632-8 Abstract: The TATA-box binding protein associated factor 1 (TAF1) protein is a key unit of the transcription factor II D complex that serves a vital function during transcription initiation. Variants of TAF1 have been associated with neurodevelopmental disorders, but TAF1’s molecular functions remain elusive. In this study, we present a five-generation family affected with X-linked intellectual disability that co-segregated with a TAF1 c.3568C>T, p.(Arg1190Cys) variant. All affected males presented with intellectual disability and dysmorphic features, while heterozygous females were asymptomatic and had completely skewed X-chromosome inactivation. We investigated the role of TAF1 and its association to neurodevelopment by creating the first complete knockout model of the TAF1 orthologue in zebrafish. A crucial function of human TAF1 during embryogenesis can be inferred from the model, demonstrating that intact taf1 is essential for embryonic development. Transcriptome analysis of taf1 zebrafish knockout revealed enrichment for genes associated with neurodevelopmental processes. In conclusion, we propose that functional TAF1 is essential for embryonic development and specifically neurodevelopmental processes. 9. Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks Authors: Anindya Gupta, Tonis Saar(1), Olev Martens(2), Yannick Le Moullec(2), Ida-Maria Sintorn (1) Eliko Tehnoloogia Arenduskeskus OU,¨ Tallinn, Estonia (2) Thomas Johann Seebeck Dept. of Electronics, Tallinn University of Technology, Estonia Journal: International journal of imaging systems and technology, 13 pages DOI: 10.1002/ima.22373 Abstract: Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error-prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer-assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidate-screening module and a false positive (FP) reduction module. The candidate-screening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features-based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro-nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well-suited FP

59 reduction module of a CAD scheme. 10. Impact of Q-Griffithsin anti-HIV microbicide gel in non-human primates: In situ analyses of epithelial and immune cell markers in rectal mucosa Authors: Gokçe¨ Gunaydın(1),¨ Gabriella Edfeldt(1), David A. Garber(2), Muhammad Asghar(1), Laura Noel-Romas(3),˜ Adam Burgener(1,3), Carolina Wahlby¨ , Lin Wang(4), Lisa C. Rohan(4,5), Patricia Guen- thner(2), James Mitchell(2), Nobuyuki Matoba(6,7,8), Janet M. McNicholl(2), Kenneth E. Palmer(6,7,8), Annelie Tjernlund(1), Kristina Broliden(1) (1) Dept. of Medicine Solna, Division of Infectious Diseases, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden (2) Laboratory Branch, Division of HIV/AIDS Prevention, National Centre for HIV/AIDS, Viral Hepatitis, Sexually Transmitted Disease and Tuberculosis Prevention, CDC, Atlanta, USA (3) National HIV and Retrovirology Labs, JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada, and Dept. of Obstetrics & Gynecology, and Dept. of Medical Mi- crobiology, University of Manitoba, Canada (4) Magee-Womens Research Institute, Pittsburgh, USA (5) School of Pharmacy, University of Pittsburgh, USA (6) Center for Predictive Medicine, University of Louisville, USA (7) Dept. of Pharmacology and Toxicology, University of Louisville, USA (8) James Graham Brown Cancer Center, University of Louisville, USA Journal: Scientific Reports volume 9, Article number: 18120 DOI: 0.1038/s41598-019-54493-4 Abstract: Natural-product derived lectins can function as potent viral inhibitors with minimal toxicity as shown in vitro and in small animal models. We here assessed the effect of rectal application of an anti- HIV lectin-based microbicide Q-Griffithsin (Q-GRFT) in rectal tissue samples from rhesus macaques. E- cadherin+ cells, CD4+ cells and total mucosal cells were assessed using in situ staining combined with a novel customized digital image analysis platform. Variations in cell numbers between baseline, placebo and Q-GRFT treated samples were analyzed using random intercept linear mixed effect models. The frequencies of rectal E-cadherin+ cells remained stable despite multiple tissue samplings and Q-GRFT gel (0.1%, 0.3% and 1%, respectively) treatment. Whereas single dose application of Q-GRFT did not affect the frequencies of rectal CD4+ cells, multi-dose Q-GRFT caused a small, but significant increase of the frequencies of intra-epithelial CD4+ cells (placebo: median 4%; 1% Q-GRFT: median 7%) and of the CD4+ lamina propria cells (placebo: median 30%; 0.1–1% Q-GRFT: median 36–39%). The resting time between sampling points were further associated with minor changes in the total and CD4+ rectal mucosal cell levels. The results add to general knowledge of in vivo evaluation of anti-HIV microbicide application concerning cellular effects in rectal mucosa. 11. Generalized convexity: The case of lineally convex Hartogs domains Author: Christer O. Kiselman Journal: Annales Polonici Mathematici, Vol. 123, pp. 319–344 DOI: 10.4064/ap180930-3-4 Abstract: Inspired by mathematical morphology we study generalized convexity and prove that certain subsets of Hartogs domains are convex in a generalized sense. 12. Language choice in scientific writing: The case of mathematics at Uppsala University and a Nordic journal Author: Christer O. Kiselman Journal: Nordisk Matematisk Tidskrift, Vol. 61, No. 2–4, pp. 111–132 Abstract: For several centuries following its founding in 1477, all lectures and dissertations at Uppsala University were in Latin. Then, during one century, from 1852 to 1953, several languages came into use: there was a transition from Latin to Swedish, and then to French, German, and English. In the paper this transition is illustrated by language choice in doctoral theses in mathematics and in a Nordic journal. 13. Werner Fenchel, a pioneer in convexity theory and a migrant scientist Author: Christer O. Kiselman Journal: Nordisk Matematisk Tidskrift, Vol. 61, No. 2–4, pp. 133–152 Abstract: Werner Fenchel was a pioneer in introducing duality in convexity theory. He got his PhD in Berlin, was forced to leave Germany, moved to Denmark, and lived during almost two years in Sweden. In

60 a private letter he explained his views on the development of duality in convexity theory and the many terms that have been used to describe it. The background for his move from Germany is sketched. 14. Tracking Microscope Performance: A Workflow to Compare Point Spread Function Evaluations Over Time Authors: Anna H. Klemm(1,2), Andreas W. Thomae(2), Katarina Wachal(2) and Steffen Dietzel(2) (1) Bioimage Informatics Facility, SciLifeLab, UU (2) Core Facility Bioimaging at the Biomedical Center and Walter-Brendel-Zentrum fur¨ Experimentelle Medizin, Ludwig-Maximilians-Univeristat¨ Munchen,¨ Germany Journal: Microscopy and Microanalysis, Vol. 25, Issue 3, pp. 699–704 DOI: 10.1017/S1431927619000060 Abstract: Routine system checks are essential for supervising the performance of an advanced light microscope. Recording and evaluating the point spread function (PSF) of a given system provides information about the resolution and imaging. We compared the performance of fluorescent and gold beads for PSF recordings. We then combined the open-source evaluation software PSFj with a newly developed KNIME pipeline named PSFtracker to create a standardized workflow to track a system’s performance over several measurements and thus over long time periods. PSFtracker produces example images of recorded PSFs, plots full-width-half-maximum (FWHM) measurements over time and creates an html file which embeds the images and plots, together with a table of results. Changes of the PSF over time are thus easily spot- ted, either in FWHM plots or in the time series of bead images which allows recognition of aberrations in the shape of the PSF. The html file, viewed in a local browser or uploaded on the web, therefore provides intuitive visualization of the state of the PSF over time. In addition, uploading of the html file on the web allows other microscopists to compare such data with their own. 15. Modeling Spatial Correlation of Transcripts with Application to Developing Pancreas Authors: Ruishan Liu(1), Marco Mignardi(2,5), Robert Jones(2), Martin Enge(2), Seung K. Kim(3), Stephen R. Quake(2,5), James Zou(4,5) (1) Dept. of Electrical Engineering, Stanford University, USA (2) Dept. of Bioengineering and Applied Physics, Stanford University, USA (3) Dept. of Developmental Biology, Stanford University, USA (4) Dept. of Biomedical Data Science, Stanford University, USA (5) Chan-Zuckerberg Biohub, San Francisco, USA Journal: Scientific Reports, Vol. 9, Article number: 5592 DOI: 10.1038/s41598-019-41951-2 Abstract: Recently high-throughput image-based transcriptomic methods were developed and enabled researchers to spatially resolve gene expression variation at the molecular level for the first time. In this work, we develop a general analysis tool to quantitatively study the spatial correlations of gene expression in fixed tissue sections. As an illustration, we analyze the spatial distribution of single mRNA molecules measured by in situ sequencing on human fetal pancreas at three developmental time points–80, 87 and 117 days post- fertilization. We develop a density profile-based method to capture the spatial relationship between gene expression and other morphological features of the tissue sample such as position of nuclei and endocrine cells of the pancreas. In addition, we build a statistical model to characterize correlations in the spatial distribution of the expression level among different genes. This model enables us to infer the inhibitory and clustering effects throughout different time points. Our analysis framework is applicable to a wide variety of spatially-resolved transcriptomic data to derive biological insights. 16. Relationship Between Endothelium-dependent Vasodilation and Fat Distribution using the new ”Imiomics” Image Analysis Technique Authors: Lars Lind(1), Robin Strand(3), Karl Michaelsson(2), Joel Kullberg(3,4), Hakan˚ Ahlstrom(3,4)¨ (1) Dept. of Medical Sciences, UU (2) Dept. of Surgical Sciences, UU (3) Division of Radiology, Dept. of Surgical Sciences, UU (4) Antaros Medical, BioVenture Hub, Molndal,¨ Sweden Journal: Nutrition, Metabolism & Cardiovascular Diseases, Vol. 29(10), pp. 1077–1086 DOI: 10.1016/j.numecd.2019.06.017 Abstract: BACKGROUND AND AIMS: We investigated how vasoreactivity in the brachial artery and the forearm resistance vessels were related to fat distribution and tissue volume, using both traditional imaging analysis

61 and a new technique, called ”Imiomics”, whereby vasoreactivity was related to each of the >2M 3D image elements included in the whole-body magnetic resonance imaging (MRI). METHODS AND RESULTS: In 326 subjects in the Prospective investigation of Obesity, Energy and Metabolism (POEM) study (all aged 50 years), endothelium-dependent vasodilation was measured by acetylcholine infusion in the brachial artery (EDV) and flow-mediated vasodilation (FMD). Fat distribution was evaluated by dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI). EDV, but not FMD, was significantly related to total fat mass, liver fat, subcutaneous (SAT) and visceral (VAT) adipose tissue in a negative fashion in women, but not in men. Using Imiomics, an inverse relationship was seen between EDV and a local tissue volume of SAT in both the upper part of the body, as well as the gluteo-femoral part and the medial parts of the legs in women. Also the size of the liver, heart and VAT was inversely related to EDV. In men, less pronounced relationships were seen. FMD was also significantly related to local tissue volume of upper-body SAT and liver fat in women, but less so in men. CONCLUSION: EDV, and to a lesser degree also FMD, were related to liver fat, SAT and VAT in women, but less so in men. Imiomics both confirmed findings from traditional methods and resulted in new, more detailed results. 17. Proof of principle study of a detailed whole-body image analysis technique, ”Imiomics”, regarding adipose and lean tissue distribution Authors: Lars Lind(1), Joel Kullberg(2,3), Hakan˚ Ahlstrom(2,3),¨ Karl Michaelsson(4), Robin Strand(2) (1) Dept. of Medical Sciences, UU (2) Division of Radiology, Dept. of Surgical Sciences, UU (3) Antaros Medical, BioVenture Hub, Molndal,¨ Sweden (4) Dept. of Surgical Sciences, UU Journal: Scientific Reports, Vol. 9, Article number 7388 DOI: 10.1038/s41598-019-43690-w Abstract: This ”proof-of-principle” study evaluates if the recently presented ”Imiomics” technique could visualize how fat and lean tissue mass are associated with local tissue volume and fat content at high/ unprecedented resolution. A whole-body quantitative water-fat MRI scan was performed in 159 men and 167 women aged 50 in the population-based POEM study. Total fat and lean mass were measured by DXA. Fat content was measured by the water-fat MRI. Fat mass and distribution measures were associated to the detailed differences in tissue volume and fat concentration throughout the body using Imiomics. Fat mass was positively correlated (r > 0.50, p < 0.05) with tissue volume in all subcutaneous areas of the body, as well as volumes of the liver, intraperitoneal fat, retroperitoneal fat and perirenal fat, but negatively to lung volume. Fat mass correlated positively with volumes of paravertebral muscles, and muscles in the ventral part of the thigh and lower limb. Fat mass was distinctly correlated with the fat content in subcutaneous adipose tissue at the trunk. Lean mass was positively related to the large skeletal muscles and the skeleton. The present study indicates the Imiomics technique to be suitable for studies of fat and lean tissue distribution, and feasible for large scale studies. 18. A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities Authors: Marie-Ange Lebre(1), Antoine Vacavant(1), Manuel Grand-Brochier(1), Hugo Rositi(1), Robin Strand, Hubert Rosier (2), Armand Abergel (1), Pascal Chabrot (1), Benoit Magnin (1) (1) Universite´ Clermont Auvergne, CHU Clermont-Ferrand, CNRS, SIGMA Clermont, Institut Pascal, F- 63000 Clermont-Ferrand, France (2) Centre Hospitalier Emile´ Roux, Le Puy-en-Velay, France Journal: Computerized Medical Imaging and Graphics, Vol. 76, Article number 101635 DOI: 10.1016/j.compmedimag.2019.05.003 Abstract: Developing methods to segment the liver in medical images, study and analyze it remains a significant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambiguous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3% on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from

62 public challenges. Finally, for evaluation of liver segmentation approaches in state of the art, robustness is not adequacy addressed with a precise definition. Another originality of this article is the introduction of a novel measure of robustness, which takes into account the liver variability at different scales. 19. Brown adipose tissue estimated with the magnetic resonance imaging fat fraction is associated with glucose metabolism in adolescents Authors: Elin Lundstrom(1),¨ Joy Ljungberg(1), Jonathan Andersson(1), Hannes Manell(2,3,4), Robin Strand(1), Anders Forslund(2,3), Peter Bergsten(2,3,4), Daniel Weghuber(5), Katharina Morwald(5),¨ Fanni Zsol- dos(5), Kurt Widhalm(5,6), Matthias Meissnitzer(7), Hakan˚ Ahlstrom(1,8),¨ Joel Kullberg(1,8) (1) Dept. of Surgical Sciences, Section of Radiology, UU (2) Dept. of Women’s and Children’s Health, UU (3) Children Obesity Clinic, Uppsala University Hospital (4) Dept. of Medical Cell Biology, UU (5) Dept. of Pediatrics, and Obesity Research Unit, Paracelsus Medical University, Salzburg, Austria (6) Dept. of Pediatrics, Medical University of Vienna, Austria (7) Dept. of Radiology, Paracelsus Medical University, Salzburg, Austria (8) Antaros Medical, BioVenture Hub, Molndal,¨ Sweden Journal: Pediatric Obesity, Vol. 14, No. 9, artikel-id e12531 DOI: 10.1111/ijpo.12531 Abstract: Background: Despite therapeutic potential against obesity and diabetes, the associations of brown adipose tissue (BAT) with glucose metabolism in young humans are relatively unexplored. Objectives: To investigate possible associations between magnetic resonance imaging (MRI) estimates of BAT and glucose metabolism, whilst considering sex, age, and adiposity, in adolescents with normal and overweight/obese phenotypes. Methods: In 143 subjects (10-20 years), MRI estimates of BAT were assessed as cervical-supraclavicular adipose tissue (sBAT) fat fraction (FF) and T*2 from water-fat MRI. FF and T*2 of neighbouring subcutaneous adipose tissue (SAT) were also assessed. Adiposity was estimated with a standardized body mass index, the waist-to-height ratio, and abdominal visceral and subcutaneous adipose tissue volumes. Glucose metabolism was represented by the 2h plasma glucose concentration, the Matsuda index, the homeostatic model assessment of insulin resistance, and the oral disposition index; obtained from oral glucose tolerance tests. Results: sBAT FF and T*2 correlated positively with adiposity before and after adjustment for sex and age. sBAT FF, but not T*2, correlated with 2h glucose and Matsuda index, also after adjustment for sex, age, and adiposity. The association with 2h glucose persisted after additional adjustment for SAT FF. Conclusions: The association between sBAT FF and 2h glucose, observed independently of sex, age, adiposity, and SAT FF, indicates a role for BAT in glucose metabolism, which potentially could influence the risk of developing diabetes. The lacking association with sBAT T*2 might be due to FF being a superior biomarker for BAT and/or to methodological limitations in the T*2 quantification. 20. Reducing the U-Net size for practical scenarios: Virus recognition in electron microscopy images Authors: Damian J. Matuszewski, Ida-Maria Sintorn(1) (1) Vironova AB, Stockholm, Sweden Journal: Computer Methods and Programs in Biomedicine, Vol. 178, pp. 31–39 DOI: 10.1016/j.cmpb.2019.05.026 Abstract: Background and objective: Convolutional neural networks (CNNs) offer human experts-like performance and in the same time they are faster and more consistent in their prediction. However, most of the proposed CNNs require an expensive state-of-the-art hardware which substantially limits their use in practical scenarios and commercial systems, especially for clinical, biomedical and other applications that require on-the-fly analysis. In this paper, we investigate the possibility of making CNNs lighter by parametrizing the architecture and decreasing the number of trainable weights of a popular CNN: U-Net. Methods: In order to demonstrate that comparable results can be achieved with substantially less trainable weights than the original U-Net we used a challenging application of a pixel-wise virus classification in Transmission Electron Microscopy images with minimal annotations (i.e. consisting only of the virus par- ticle centers or centerlines). We explored 4 U-Net hyper-parameters: the number of base feature maps, the feature maps multiplier, the number of the encoding-decoding levels and the number of feature maps in the

63 last 2 convolutional layers. Results: Our experiments lead to two main conclusions: 1) the architecture hyper-parameters are pivotal if less trainable weights are to be used, and 2) if there is no restriction on the trainable weights number using a deeper network generally gives better results. However, training larger networks takes longer, typically requires more data and such networks are also more prone to overfitting. Our best model achieved an accuracy of 82.2% which is similar to the original U-Net while using nearly 4 times less trainable weights (7.8 M in comparison to 31.0 M). We also present a network with ¡ 2M trainable weights that achieved an accuracy of 76.4%. Conclusions: The proposed U-Net hyper-parameter exploration can be adapted to other CNNs and other applications. It allows a comprehensive CNN architecture designing with the aim of a more efficient trainable weight use. Making the networks faster and lighter is crucial for their implementation in many practical applications. In addition, a lighter network ought to be less prone to over-fitting and hence generalize better. 21. Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval Authors: Shrikant A. Mehre(1), Ashis Kumar Dhara(1), Mandeep Garg(2), Naveen Kalra(2), Niranjan Khandelwal(2), Sudipta Mukhopadhyay(1) (1) Dept. of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharag- pur, India (2) Dept. of Radiodiagnosis and Imaging, Post-graduate Institute of Medical Education and Research, Chandigarh, India Journal: Journal of Digital Imaging, Vol. 32, pp. 362–385 DOI: 10.1007/s10278-018-0136-1 Abstract: Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR). The performance of the classifier is always superior to SDR, which leans researchers towards conventional classifier-based retrieval (CCBR). While CCBR improves the average precision and provides 100% precision for correct classification, it fails for misclassification leading to zero retrieval precision. The class membership-based retrieval (CMR) is found to bridge this gap for texture-based retrieval. Here, CMR is proposed for nodule retrieval using shape-, margin-, and texture-based features. It is found again that optimal feature set is important for the classifier used in CMR as well as for the feature set used for retrieval, which may lead to different feature sets. The proposed system is evaluated using two independent databases from two conti- nents: a public database LIDC/IDRI and a private database PGIMER-IITKGP, using three distance metrics, i.e.,Canberra, City block, and Euclidean. The proposed CMR-based retrieval system with optimal feature sets performs better than CCBR and SDR with optimal features in terms of average precision. Apart from average precision and standard deviation of precision, the fraction of queries with zero precision retrieval is also measured. 22. The impact of interactive visualization on trade-off-based geospatial decision-making Authors: Goran Milutinovic(1),´ Ulla Ahonen-Jonnarth(1) , Stefan Seipel(1), Sven Anders Brandt(1) (1) Dept. of Computer and Geospatial Sciences, University of Gavle¨ Journal: International Journal of Geographical Information Science, Vol. 33, Issue 10, pp. 2094–2123 DOI: 10.1080/13658816.2019.1613547 Abstract: In a previous work we developed GISwaps, a novel method for geospatial decision-making based on Even Swaps. In this paper, we present the results of an evaluation of a visualization framework integrated with this method, implemented within a decision support system. This evaluation is based on two different studies. In the quantitative study, 15 student participants used GISwaps with no visual features, and 15 participants used GISwaps with the integrated visual framework, as the tool in a solar farm site location case study. The results of the quantitative evaluation show positive impact of the visualization in terms of increased coherency in trade-offs. The results also show a statistically significant difference in average trade-off values between the groups, with users from the non-visual group setting on average 20% higher trade-off values compared with the users in the visual group. In the qualitative study, we had one expert

64 in GIS, two experts in decision-making and two experts in solar energy as a focus user group. Data in this study were obtained by observations and semi-structured interviews with the participants. The impact of the visualization framework was assessed positively by all participants in the expert group. 23. In search of the scribe: Letter spotting as a tool for identifying scribes in large handwritten text corpora Authors: Lasse Martensson(1),˚ Ekta Vats, Anders Hast, Alicia Fornes(2)´ (1) Stockholm University (2) Universitat Autonoma` de Barcelona Journal: Human IT, Vol. 14, No. 2, pp. 95–120 Abstract: In this article, a form of the so-called word spotting-method is used on a large set of handwritten documents in order to identify those that contain script of similar execution. The point of departure for the investigation is the mediaeval Swedish manuscript Cod. Holm. D 3. The main scribe of this manuscript has yet not been identified in other documents. The current attempt aims at localising other documents that display a large degree of similarity in the characteristics of the script, these being possible candidates for being executed by the same hand. For this purpose, the method of word spotting has been employed, focusing on individual letters, and therefore the process is referred to as letter spotting in the article. In this process, a set of ‘g’:s, ‘h’:s and ‘k’:s have been selected as templates, and then a search has been made for close matches among the mediaeval Swedish charters. The search resulted in a number of charters that displayed great similarities with the manuscript D 3. The used letter spotting method thus proofed to be a very efficient sorting tool localising similar script samples. 24. Rational nanotoolbox with theranostic potential for medicated pro-regenerative corneal implants Authors: Hirak K. Patra(1,2,3), Mohammad Azharuddin(2), Mohammad M. Islam(2,4), Georgia Papa- pavlou(2), Suryyani Deb(2,5,6), Johannes Osterrieth(1), Geyunjian Harry Zhu(1), Thobias Romu(7), Ashis K. Dhara(8), Mohammad J. Jafari(9), Amineh Ghaderi(10), Jorma Hinkula(2), Madhavan S. Rajan(11), Nigel K. H. Slater(1) (1) Dept. of Chemical Engineering and Biotechnology, Cambridge University, UK (2) Dept. of Clinical and Experimental Medicine, Linkoping¨ University (3) Wolfson College, University of Cambridge, UK (4) Massachusetts Eye and Ear and Schepens Eye Research Institute, Dept. of Ophthalmology, Harvard Medical School, Boston, (5) Dept. of Biochemistry, University of Calcutta, India (6) Dept. of Biotechnology, Maulana Abul Kalam Azad University of Technology (MAKAUT), West Ben- gal, India (7) Centre for Medical Image Science and Visualization, Linkoping¨ University (8) Dept. of Electrical Engineering, National Institute of Technology Durgapur, West Bengal, India (9) Dept. of Physics, Chemistry and Biology (IFM), Linkoping¨ University (10) Dept. of Oncology-Pathology, Karolinska Institute, Stockholm (11) Dept. of Ophthalmology, Cambridge University Hospitals NHS Trust and Vision and Eye Research Institute (VERI), Anglia Ruskin University, Cambridge, UK Journal: Advanced Functional Materials, Vol. 29, No. 38, article-id 1903760 DOI: 10.1002/adfm.201903760 Abstract: Cornea diseases are a leading cause of blindness and the disease burden is exacerbated by the increasing shortage around the world for cadaveric donor corneas. Despite the advances in the field of regenerative medicine, successful transplantation of laboratory-made artificial corneas is not fully realized in clinical practice. The causes of failure of such artificial corneal implants are multifactorial and include la- tent infections from viruses and other microbes, enzyme overexpression, implant degradation, extrusion or delayed epithelial regeneration. Therefore, there is an urgent unmet need for developing customized corneal implants to suit the host environment and counter the effects of inflammation or infection, which are able to track early signs of implant failure in situ. This work reports a nanotoolbox comprising tools for protection from infection, promotion of regeneration, and noninvasive monitoring of the in situ corneal environment. These nanosystems can be incorporated within pro-regenerative biosynthetic implants, transforming them into theranostic devices, which are able to respond to biological changes following implantation. 25. Average volume reference space for large scale registration of whole-body magnetic resonance images Authors: Martino Pilia(1), Joel Kullberg(1,2), Hakan˚ Ahlstrom(1,2),¨ Filip Malmberg(1), Simon Ekstrom(1),¨ Robin Strand(1)

65 (1) Dept. of Surgical Sciences, UU (2) Antaros Medical, Uppsala Journal: PLoS ONE, Vol. 14, No. 10, article-id e0222700 DOI: 10.1371/journal.pone.0222700 Abstract: Background and objectives: The construction of whole-body magnetic resonance (MR) imaging atlases allows to perform statistical analysis with applications in anomaly detection, longitudinal, and correlation studies. Atlas-based methods require a common coordinate system to which all the subjects are mapped through image registration. Optimisation of the reference space is an important aspect that affects the subsequent analysis of the registered data, and having a reference space that is neutral with respect to local tissue volume is valuable in correlation studies. The purpose of this work is to generate a reference space for whole-body imaging that has zero voxel-wise average volume change when mapped to a cohort. Methods: This work proposes an approach to register multiple whole-body images to a common template using volume changes to generate a synthetic reference space, starting with an initial reference and refining it by warping it with a deformation that brings the voxel-wise average volume change associated to the mappings of all the images in the cohort to zero. Results: Experiments on fat/water separated whole-body MR images show how the method effectively generates a reference space neutral with respect to volume changes, without reducing the quality of the registration nor introducing artefacts in the anatomy, while providing better alignment when compared to an implicit reference groupwise approach. Conclusions: The proposed method allows to quickly generate a reference space neutral with respect to local volume changes, that retains the registration quality of a sharp template, and that can be used for statistical analysis of voxel-wise correlations in large datasets of whole-body image data. 26. Capturing and characterizing human activities using building locations in America Authors: Zheng Ren(1), Bin Jiang(1), Stefan Seipel(1) (1) Division of GIScience, Faculty of Engineering and Sustainable Development, University of Gavle¨ Journal: ISPRS International Journal of Geo-Information, Vol. 8, No. 5, article 200 DOI: 10.3390/ijgi8050200 Abstract: Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activities can be well represented by social media data remains uncertain. On the other hand, buildings or architectures are permanent and reliable representations of human activities collectively through historical footprints. This study aims to use the big data of US building footprints to investigate the reliability of social media users for human activity prediction. We created spatial clusters from 125 million buildings and 1.48 million Twitter points in the US. We further examined and compared the spatial and statistical distribution of clusters at both country and city levels. The result of this study shows that both building and Twitter data spatial clusters show the scaling pattern measured by the scale of spatial clusters, respectively, characterized by the number points inside clusters and the area of clusters. More specifically, at the country level, the statistical distribution of the building spatial clusters fits power law distribution. Inside the four largest cities, the hotspots are power-law-distributed with the power law exponent around 2.0, meaning that they also follow the Zipf’s law. The correlations between the number of buildings and the number of tweets are very plausible, with the r square ranging from 0.53 to 0.74. The high correlation and the similarity of two datasets in terms of spatial and statistical distribution suggest that, although social media users are only a proportion of the entire population, the spatial clusters from geographical big data is a good and accurate representation of overall human activities. This study also indicates that using an improved method for spatial clustering is more suitable for big data analysis than the conventional clustering methods based on Euclidean geometry. 27. Region-by-region analysis of PET, MRI, and histology in en bloc-resected oligodendrogliomas reveals intra-tumoral heterogeneity Authors: Kenney Roy Roodakker(1), Ali Alhuseinalkhudhur, Mohammed Al-Jaff, Maria Georganaki(3), Maria Zetterling(4), Shala G. Berntsson(1), Torsten Danfors(2), Robin Strand(2), Per-Henrik Edqvist(3), Anna Dimberg(3), Elna-Marie Larsson(2,5), Anja Smits(1,6)

66 (1) Dept. of Neuroscience, Neurology, UU, University Hospital (2) Dept. of Surgical Sciences, Radiology, UU (3) Dept. of Immunology, Genetics and Pathology, Rudbeck Laboratory, UU (4) Dept. of Neuroscience, Section of Neurosurgery, UU (5) Dept. of Radiology, Uppsala University Hospital, UU (6) Institute of Neuroscience and Physiology, Dept. of Clinical Neuroscience, Sahlgrenska Academy, Uni- versity of Gothenburg Journal: European Journal of Nuclear Medicine and Molecular Imaging, Vol. 46, pp. 569–579 DOI: 10.1007/s00259-018-4107-z Abstract: Purpose: Oligodendrogliomas are heterogeneous tumors in terms of imaging appearance, and a deeper understanding of the histopathological tumor characteristics in correlation to imaging parameters is needed. We used PET-to-MRI-to-histology co-registration with the aim of studying intra-tumoral 11C-methionine (MET) uptake in relation to tumor perfusion and the protein expression of histological cell markers in corresponding areas. Methods: Consecutive histological sections of four tumors covering the entire en bloc-removed tumor were immunostained with antibodies against IDH1-mutated protein (tumor cells), Ki67 (proliferating cells), and CD34 (blood vessels). Software was developed for anatomical landmarks-based co-registration of subsequent histological images, which were overlaid on corresponding MET PET scans and MRI perfusion maps. Regions of interest (ROIs) on PET were selected throughout the entire tumor volume, covering hot spot areas, areas adjacent to hot spots, and tumor borders with infiltrating zone. Tumor-to-normal tissue (T/N) ratios of MET uptake and mean relative cerebral blood volume (rCBV) were measured in the ROIs and protein expression of histological cell markers was quantified in corresponding regions. Statistical correlations were calculated between MET uptake, rCBV, and quantified protein expression. Results: A total of 84 ROIs were selected in four oligodendrogliomas. A significant correlation (p < 0.05) between MET uptake and tumor cell density was demonstrated in all tumors separately. In two tumors, MET correlated with the density of proliferating cells and vessel cell density. There were no significant correlations between MET uptake and rCBV, and between rCBV and histological cell markers. Conclusions: The MET uptake in hot spots, outside hotspots, and in infiltrating tumor edges unanimously reflects tumor cell density. The correlation between MET uptake and vessel density and density of proliferating cells is less stringent in infiltrating tumor edges and is probably more susceptible to artifacts caused by larger blood vessels surrounding the tumor. Although based on a limited number of samples, this study provides histological proof for MET as an indicator of tumor cell density and for the lack of statistically significant correlations between rCBV and histological cell markers in oligodendrogliomas.

28. A strategy for OCT estimation of the optic nerve head pigment epithelium central limit-inner limit of the retina minimal distance, PIMD-2⇡ Authors: Camilla Sandberg Melin(1), Filip Malmberg Per G. Soderberg(1)¨ (1) Gullstrand lab, Ophthalmology, Dept. of Neuroscience, UU Journal: Acta Ophthalmologica, Vol. 97, Issue 2, pp. 208–213 DOI: 10.1111/aos.13908 Abstract: Purpose: To develop a semi-automatic algorithm for estimation of pigment epithelium central limit-inner limit of the retina minimal distance averaged over 2⇡ radians (PIMD-2⇡) and to estimate the precision of the algorithm. Further, the variances in estimates of PIMD-2⇡ were to be estimated in a pilot sample of glaucomatous eyes Methods: Three-dimensional cubes of the optic nerve head (ONH) were captured with a commercial SD- OCT device. Raw cube data were exported for semi-automatic segmentation. The inner limit of the retina was automatically detected. Custom software aided the delineation of the ONH pigment epithelium central limit resolved in 500 evenly distributed radii. Sources of variation in PIMD estimates were analysed with an analysis of variance. Results: The estimated variance for segmentations and angles was 130 µm2 and 1280 µm2, respectively. Considering averaging eight segmentations, a 95 % conﬁdence interval for mean PIMD-24⇡ was estimated to 212 10 µm (df = 7). The coefﬁcient of variation for segmentation was estimated at 0.05. In the ± glaucomatous eyes, the within-subject variance for captured volumes and for segmentations within volumes was 10 µm2 and 50 µm2, respectively.

67 Conclusion: The developed semi-automatic algorithm enables estimation of PIMD-2⇡ in glaucomatous eyes with relevant precision using few segmentations of each captured volume. 29. A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis Authors: Therese Sjoholm(1),¨ Simon Ekstrom(1),¨ Robin Strand(1), Hakan˚ Ahlstrom(1,2),¨ Lars Lind(3), Filip Malmberg(1), Joel Kullberg(1,2) (1) Dept. of Surgical Sciences, UU (2) Antaros Medical AB, Molndal¨ (3) Dept. of Medical Sciences, UU Journal: Scientific Reports, Vol.9, Article number 6158 DOI: 10.1038/s41598-019-42613-z Abstract: Quantitative multiparametric imaging is a potential key application for Positron Emission To- mography/Magnetic Resonance (PET/MR) hybrid imaging. To enable objective and automatic voxel-based multiparametric analysis in whole-body applications, the purpose of this study was to develop a multimodal- ity whole-body atlas of functional 18F-fluorodeoxyglucose (FDG) PET and anatomical fat-water MR data of adults. Image registration was used to transform PET/MR images of healthy control subjects into male and female reference spaces, producing a fat-water MR, local tissue volume and FDG PET whole-body normal atlas consisting of 12 male (66.6±6.3 years) and 15 female (69.5 3.6 years) subjects. Manual seg- ± mentations of tissues and organs in the male and female reference spaces confirmed that the atlas contained adequate physiological and anatomical values. The atlas was applied in two anomaly detection tasks as proof of concept. The first task automatically detected anomalies in two subjects with suspected malignant disease using FDG data. The second task successfully detected abnormal liver fat infiltration in one subject using fat fraction data. 30. Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard Authors: Dorothee´ Vallot(1), Sigit Adinugroho(2), Robin Strand, Penelope How(3), Rickard Petters- son(1), Douglas I. Benn(4), Nicholas R. J. Hulton(5) (1) Dept. of Earth Sciences, UU (2) Faculty of Computer Science, Brawijaya University, Malang, Indonesia (3) Institute of Geography, School of GeoSciences, University of Edinburgh, UK (4) School of Geography and Geosciences, University St Andrews, UK (5) Dept. of Arctic Geology, UNIS, The University Center in Svalbard, Norway Journal: Geoscientific Instrumentation Methods and Data Systems, Vol. 8, pp. 113–127 DOI: 10.5194/gi-8-113-2019 Abstract: Calving is an important process in glacier systems terminating in the ocean, and more observations are needed to improve our understanding of the undergoing processes and parameterize calving in larger-scale models. Time-lapse cameras are good tools for monitoring calving fronts of glaciers and they have been used widely where conditions are favourable. However, automatic image analysis to detect and calculate the size of calving events has not been developed so far. Here, we present a method that fills this gap using image analysis tools. First, the calving front is segmented. Second, changes between two images are detected and a mask is produced to delimit the calving event. Third, we calculate the area given the front and camera positions as well as camera characteristics. To illustrate our method, we analyse two image time series from two cameras placed at different locations in 2014 and 2015 and compare the automatic detection results to a manual detection. We find a good match when the weather is favourable, but the method fails with dense fog or high illumination conditions. Furthermore, results show that calving events are more likely to occur (i) close to where subglacial meltwater plumes have been observed to rise at the front and (ii) close to one another. 31. SynQuant: an automatic tool to quantify synapses from microscopy images Authors: Yizhi Wang(1), Congchao Wang(1), Petter Ranefall(2), Gerard Joey Broussard(3), Yinxue Wang(1), Guilai Shi(4), Boyu Lyu(1), Chiung-Ting Wu(1), Yue Wang(1), Lin Tian(4), Guoqiang Yu(1) (1) Bradley Dept. of Electrical and Computer Engineering, Virginia Polytechnic Institute and State Univer- sity, Blacksburg, USA (2) Bioimage Informatics Facility, SciLifeLab, UU (3) Dept. of Molecular Biology and Princeton Neuroscience Institute, Princeton University, Princeton, USA (4) Dept. of Biochemistry and Molecular Medicine, University of California Davis School of Medicine, Sacramento, USA Journal: Bioinformatics, btz760

68 DOI: 10.1093/bioinformatics/btz760 Abstract: Motivation: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding sat- isfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. Results: We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. 32. Correcting Exorbitism by Monobloc Frontofacial Advancement in Crouzon-Pfeiffer Syndrome: An Age-Specific, Time-Related, Controlled Study Authors: Benjamin L. Way, Roman Khonsari, Tharsika Karunakaran, Johan Nysjo,¨ Ingela Nystrom¨ , David Dunaway, Robert Evans, Richard Hayward, Jonathan Britto (1) Craniofacial Unit, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK (2) Assistance Publique Hopitauxˆ de Paris, Hopitalˆ Necker Enfants-Malades, Service de Chirurgie Maxillo- faciale et Plastique, Universite´ Paris Descartes, Paris, France Journal: Plastic and Reconstructive Surgery. 143(1):121e–132e DOI: 10.1097/PRS.0000000000005105 Abstract: Background: In FGFR2 craniosynostosis, midfacial hypoplasia features oculo-orbital disproportion and symptomatic exorbitism. Clinical consequences may mandate surgery at a young age to prevent globe subluxation, corneal ulceration, and potential loss of vision. Monobloc osteotomy and distraction osteogenesis (monobloc distraction) seek to correct exorbitism. A report of the age-related impact of monobloc osteotomy and distraction osteogenesis on orbital volume, globe volume, and globe protrusion is presented. Methods: Computed tomographic scan data from 28 Crouzon-Pfeiffer patients were assessed at preoper- ative, early postoperative, and 1-year follow-up time points. Orbital volumes, globe volumes, and globe protrusions were measured by manual and semiautomatic segmentation techniques, and these were compared to 40 age-matched controls. Results: Crouzon-Pfeiffer syndrome orbital volumes are significantly small, and are significantly overex- panded by distraction to endpoints correcting symptomatic exorbitism. Globe volumes are significantly larger than controls under 5 years, do not independently correlate with globe protrusion, and are unaf- fected by surgery. Correlation between orbital volume expansion and reduction of globe protrusion is not significant. Age-related variations of postoperative growth potential occur to 1 year postoperatively. The Crouzon-Pfeiffer syndrome FGFR2 orbit exhibits early growth acceleration followed by premature growth arrest at 10 to 14 years. Conclusions: Orbital volume expansion by monobloc osteotomy and distraction osteogenesis is not the sole determinant of reduced globe protrusion. Mean volume relapse of the orbit at 1 year is insignificant across the series. Derived Crouzon-Pfeiffer growth curves suggest that “early functional monobloc” in in- fants occurs on a background of dynamic orbital growth, which remains programmed to a Crouzon-Pfeiffer FGFR2 phenotype and aligns with the incidence of delayed clinical regression and later secondary surgery. 33. In situ quantification of individual mRNA transcripts in melanocytes discloses gene regulation of relevance to specifiation Authors: Chi-Chih Wu(1,4), Axel Klaesson(2), Julia Buskas(1,3,4), Petter Ranefall(4), Reza Mirzazadeh(5), Ola Soderberg(2),¨ Jochen B. W. Wolf(6) (1) Dept. of Evolutionary Biology, UU

69 (2) Dept. of Pharmaceutical Biosciences, UU (3) Dept. of Physics, Chemistry and Biology (IFM), Linkoping¨ University (4) Bioimage Informatics Facility, SciLifeLab, UU (5) Dept. of Medical Biochemistry and Biophysics and SciLifeLab, Karolinska Institutet, Stockholm (6) Division of Evolutionary Biology, Faculty of Biology, LMU Munich, Germany. Journal: Journal of Experimental Biology, 222, jeb194431 DOI: 10.1242/jeb.194431 Abstract: Functional validation of candidate genes involved in adaptation and speciation remains challenging. Here, we exemplify the utility of a method quantifying individual mRNA transcripts in revealing the molecular basis of divergence in feather pigment synthesis during early-stage speciation in crows. Using a padlock probe assay combined with rolling circle amplification, we quantified cell-type-specific gene expression in the histological context of growing feather follicles. Expression of Tyrosinase Related Protein 1 (TYRP1), Solute Carrier Family 45 member 2 (SLC45A2) and Hematopoietic Prostaglandin D Synthase (HPGDS) was melanocyte-limited and significantly reduced in follicles from hooded crow, explaining the substantially lower eumelanin content in grey versus black feathers. The central upstream Melanocyte In- ducing Transcription Factor (MITF) only showed differential expression specific to melanocytes – a feature not captured by bulk RNA-seq. Overall, this study provides insight into the molecular basis of an evolutionary young transition in pigment synthesis, and demonstrates the power of histologically explicit, statistically substantiated single-cell gene expression quantification for functional genetic inference in natural popula- tions. 34. Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information Authors: Johan Ofverstedt,¨ Joakim Lindblad, Natasaˇ Sladoje Journal: IEEE Transactions on Image Processing, Vol. 28, No. 7, pp. 3584–3597 DOI: 10.1109/TIP.2019.2899947 Abstract: Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with the high affinity between images. The properties of the used measures are vital for the robustness and accuracy of the registration. In this paper, a symmetric, intensity interpolation- free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit. The method is also empirically shown to have a low computational cost, making it practical for real applications. The source code is available.

70 6.3 Refereed conference proceedings 1. Super-Resolution Reconstruction of Transmission Electron Microscopy Images Using Deep Learning Authors: Amit Suveer, Anindya Gupta, Gustaf Kylberg(1), Ida-Maria Sintorn(1) (1) Vironova AB, Stockholm In Proceedings: IEEE 16th International Symposium on Biomedical Imaging (ISBI’19), Italy, 2019, pp. 548–551 Abstract: Deep learning techniques have shown promising outcomes in single image super-resolution (SR) reconstruction from noisy and blurry low resolution data. The SR reconstruction can cater the fundamental. limitations of transmission electron microscopy (TEM) imaging to potentially attain a balance among the trade-offs like imaging-speed, spatial/temporal resolution, and dose/exposure-time, which is often difﬁcult to achieve simultaneously otherwise. In this work, we present a convolutional neural network (CNN) model, utilizing both local and global skip connections, aiming for 4 x SR reconstruction of TEM images. We used exact image pairs of a calibration grid to generate our training and independent testing datasets. The results are compared and discussed using models trained on synthetic (downsampled) and real data from the calibration grid. We also compare the variants of the proposed network with well-known classical interpolations techniques. Finally, we investigate the domain adaptation capacity of the CNN-based model by testing it on TEM images of a cilia sample, having different image characteristics as compared to the calibration-grid.

2. Stochastic Distance Transform Authors: Johan Ofverstedt,¨ Joakim Lindblad(1), Natasaˇ Sladoje(1) (1) Mathematical Institute of the Serbian Academy of Sciences and Arts, Belgrade, Serbia In Proceedings: 21th International Conference on Discrete Geometry for Computer Imagery (DGCI), Lec- ture Notes in Computer Science, Vol. 11414, pp. 75–86, France DOI: 10.1007/978-3-030-14085-4 7 Abstract: Distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise. This has a strong negative impact on the accuracy of the DT, which is highly sensitive to spurious noise points. In this study, we consider images represented as discrete random sets and observe statistics of DT computed on such representations. We, thus, deﬁne a stochastic distance transform (SDT), which has an adjustable robustness to noise. Both a stochastic Monte Carlo method and a deterministic method for computing the SDT are proposed and compared. Through a series of empirical tests, we demonstrate that the SDT is effective not only in improving the accuracy of the computed distances in the presence of noise, but also in improving the performance of template matching and watershed segmentation of partially overlapping objects, which are examples of typical applications where DTs are utilized.

3. On the Effectiveness of Generative Adversarial Networks as HEp-2 Image Augmentation Tool Authors: Toma´sˇ Majtner(1), Buda Bajic(2),´ Joakim Lindblad(3), Natasaˇ Sladoje(3), Victoria Blanes- Vidal(1), Esmaeil S. Nadimi(1) (1) Group of Machine Learning and AI, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark (2) Faculty of Technical Sciences, University of Novi Sad, Serbia (3) Mathematical Institute of the Serbian Academy of Sciences and Arts, Belgrade, Serbia In Proceedings: 21st Scandinavian Conference on Image Analysis (SCIA), LNCS, Vol. 11482, pp. 439– 451, Norrkoping,¨ Sweden DOI: 10.1007/978-3-030-20205-7 36 Abstract: One of the big challenges in the recognition of biomedical samples is the lack of large annotated datasets. Their relatively small size, when compared to datasets like ImageNet, typically leads to problems with efficient training of current machine learning algorithms. However, the recent development of generative adversarial networks (GANs) appears to be a step towards addressing this issue. In this study, we focus on one instance of GANs, which is known as deep convolutio nal generative adversarial network (DCGAN). It gained a lot of attention recently because of its stability in generating realistic artificial images. Our article explores the possibilities of using DCGANs for generating HEp-2 images. We trained multiple DCGANs and generated several datasets of HEp-2 images. Subsequently, we combined them with traditional augmentation and evaluated over three different deep learning configurations. Our article demonstrates high visual quality of generated images, which is also supported by state-of-the-art classification results.

71 4. Mathematical Morphology on Irregularly Sampled Data Applied to Segmentation of 3D Point Clouds of Urban Scenes Authors: Teo Asplund, Andres´ Serna(1), Beatriz Marcotegui(2), Robin Strand, Cris L. Luengo Hen- driks(3) (1) Terra3D, Paris, France (2) MINES ParisTech, PSL Research University, CMM - Centre for Mathematical Morphology, Fontainebleau, France (3) Flagship Biosciences Inc., Westminster, USA In Proceedings: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2019), Lecture Notes in Computer Science, Vol. 11564, pp. 375–387 DOI: 10.1007/978-3-030-20867-7 29 Abstract: This paper proposes an extension of mathematical morphology on irregularly sampled signals to 3D point clouds. The proposed method is applied to the segmentation of urban scenes to show its applica- bility to the analysis of point cloud data. Applying the proposed operators has the desirable side-effect of homogenizing signals that are sampled heterogeneously. In experiments we show that the proposed segmentation algorithm yields good results on the Paris-rue-Madame database and is robust in terms of sampling density, i.e. yielding similar labelings for more sparse samplings of the same scene. 5. Face Recognition - A One-Shot Learning Perspective Authors: Sukalpa Chanda(1), Asish Chakrapani(2),Anders Brun, Anders Hast, Umapada Pal(2), David Doermann(3) (1) Dept. of Information Technology, Østfold University College, Norway (2) Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, India (3) Computer Science and Engineering, University at Buffalo, USA In Proceedings: 15th IEEE Conference on Signal Image Technology and Internet based Systems, pp. 113– 119 Abstract: Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classiﬁcation problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nev- ertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems. 6. Finding Logo and Seal in Historical Document Images - An Object Detection based Approach Authors: Sukalpa Chanda(1), Prashant Kumar Prasad(2), Anders Hast, Anders Brun, Lasse Martensson(3),˚ Umapada Pal(4) (1) Faculty of Computer Science, Østfold University College, Halden, Norway (2) RCC Institute of Information Technology, Kolkata, India (3) Dept. of Scandinavian Languages, UU (4) Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India In Proceedings: The 5th Asian Conference on Pattern Recognition (ACPR 2019), pp. 821–834 DOI: 10.1007/978-3-030-41404-7 58 Abstract: Logo and Seal serves the purpose of authenticating and referring to the source of a document. This strategy was also prevalent in the medieval period. Different algorithm exists for detection of logo and seal in document images. A close look into the present state-of-the-art methods reveals that those methods were focused toward detection of logo and seal in contemporary document images. However, such methods are likely to underperform while dealing with historical documents. This is due to the fact that historical documents are attributed with additional challenges like extra noise, bleed-through effect, blurred foreground elements and low contrast. The proposed method frames the problem of the logo and seals detection in an object detection framework. Using a deep-learning technique it counters earlier mentioned problems and evades the need for any pre-processing stage like layout analysis and/or binarization in the system pipeline. The experiments were conducted on historical images from 12th to the 16th century and the results obtained were very encouraging for detecting logo in historical document images. To the best of our knowledge,

72 this is the first attempt on logo detection in historical document images using an object-detection based approach. 7. Making large collections of handwritten material easily accessible and searchable Authors: Anders Hast, Per Cullhed(1), Ekta Vats, Matteo Abrate(2) (1) University Library, UU (2) Institute of Informatics and Telematics, CNR, Pisa, Italy In Proceedings: Italian Research Conference on Digital Libraries - Digital Libraries: Supporting Open Sci- ence (IRCDL 2019), Communications in Computer and Information Science (CCIS), Vol. 988, pp. 18–28 DOI: 10.1007/978-3-030-11226-4 2 Abstract: Libraries and cultural organisations contain a rich amount of digitised historical handwritten material in the form of scanned images. A vast majority of this material has not been transcribed yet, owing to technological challenges and lack of expertise. This renders the task of making these historical collections available for public access challenging, especially in performing a simple text search across the collection. Machine learning based methods for handwritten text recognition are gaining importance these days, which require huge amount of pre-transcribed texts for training the system. However, it is impractical to have access to several thousands of pre-transcribed documents due to adversities transcribers face. Therefore, this paper presents a training-free word spotting algorithm as an alternative for handwritten text transcription, where case studies on Alvin (Swedish repository) and Clavius on the Web are presented. The main focus of this work is on discussing prospects of making materials in the Alvin platform and Clavius on the Web easily searchable using a word spotting based handwritten text recognition system. 8. Embedded Prototype Subspace Classification: A subspace learning framework Authors: Anders Hast, Mats Lind(1), Ekta Vats (1) Dept. of Information Technology, UU In Proceedings: International Conference on Computer Analysis of Images and Patterns CAIP 2019: Com- puter Analysis of Images and Patterns, Lecture Notes in Computer Science (LNCS), Vol. 11679, pp. 581– 592 DOI: 10.1007/978-3-030-29891-3 51 Abstract: Handwritten text recognition is a daunting task, due to complex characteristics of handwritten letters. Deep learning based methods have achieved significant advances in recognizing challenging handwritten texts because of its ability to learn and accurately classify intricate patterns. However, there are some limitations of deep learning, such as lack of well-defined mathematical model, black-box learning mechanism, etc., which pose challenges. This paper aims at going beyond the black-box learning and proposes a novel learning framework called as Embedded Prototype Subspace Classification, that is based on the well-known subspace method, to recognise handwritten letters in a fast and efficient manner. The effectiveness of the proposed framework is empirically evaluated on popular datasets using standard evaluation measures. 9. Creating an Atlas over Handwritten Script Signs Authors: Anders Hast, Lasse Martensson(1),˚ Ekta Vats, Raphaela Heil (1) Dept. of Swedish Language and Multilingualism, Stockholm University In Proceedings: Digital Humanities in the Nordic Countries Abstract: A framework for interactive visualization of script characteristics, as present in the form of handwritten letters, is proposed in this work. The basic idea behind this investigation is to lay the foundations for creating a comprehensive atlas over letter forms extracted from a large collection of handwritten documents, with minimal human guidance. The visualization of the results is based on the atlas metaphor and uses the t-SNE visualization method for creating island-like clusters that can be investigated using the proposed visualization framework. By changing a scale parameter one can investigate the dataset on different levels, i.e different sizes of the clusters. 10. Optimization of max-norm objective functions in image processing and computer vision Authors: Filip Malmberg, Krzysztof Ciesielski(1,2),Robin Strand (1) Dept. of Mathematics, West Virginia University, Morgantown, USA (2) Dept. of Radiology, MIPG University of Pennsylvania, USA In Proceedings: International Conference on Discrete Geometry for Computer Imagery (DGCI 2019), Lec- ture Notes in Computer Science Vol. 11414, pp. 206–218 DOI: 10.1007/978-3-030-14085-4 17

73 Abstract: Many fundamental problems in image processing and computer vision, such as image filtering, segmentation, registration, and stereo vision, can naturally be formulated as optimization problems. We consider binary labeling problems where the objective function is defined as the max-norm over a set of variables. It is well known that for a limited subclass of such problems, globally optimal solutions can be found via watershed cuts, i.e., cuts by optimum spanning forests. Here, we propose a new algorithm for optimizing a broader class of such problems. We prove that the proposed algorithm returns a globally optimal labeling, provided that the objective function satisfies certain given conditions, analogous to the submodularity conditions encountered in min-cut/max-flow optimization. The proposed method is highly efficient, with quasi-linear computational complexity. 11. Distance Transform Based on Weight Sequences Authors: Benedek Nagy(1), Robin Strand, Nicolas Normand(2) (1) Eastern Mediterranean University, Famagusta, North Cyprus, via Mersin-10, Turkey (2) Universite´ de Nantes, LS2N UMR CNRS 6004, France In Proceedings: International Conference on Discrete Geometry for Computer Imagery (DGCI 2019), Lec- ture Notes in Computer Science Vol. 11414, pp. 62–74 DOI: 10.1007/978-3-030-14085-4 6 Abstract: There is a continuous effort to develop the theory and methods for computing digital distance functions, and to lower the rotational dependency of distance functions. Working on the digital space, e.g., on the square grid,digital distance functions are defined by minimal costpaths, which can be processed (back-tracked etc.) without any errors or approximations. Recently, digital distance functions defined by weight sequences, which is a concept allowing multiple types of weighted steps combined with neighborhood sequences, were developed. With appropriate weight sequences, the distance between points on the perimeter of a square and the center of the square (i.e., for squares of a given size the weight sequence can be easily computed) are exactly the Euclidean distance for these distances based on weight sequences. How- ever, distances based on weight sequences may not fulfill the triangular inequality. In this paper, continuing the research, we provide a sufficient condition for weight sequences to provide metric distance. Further, we present an algorithm to compute the distance transform based on these distances. Optimization results are also shown for the approximation of the Euclidean distance inside the given square. 12. New Definition of Quality-Scale Robustness for Image Processing Algorithms, with Generalized Un- certainty Modeling, Applied to Denoising and Segmentation Authors: Antoine Vacavant(1), Marie-Ange Lebre(1), Hugo Rositi(1), Manuel Grand-Brochier(1), Robin Strand (1) Universite´ Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, Clermont-Ferrand, France In Proceedings: Reproducible Research in Pattern Recognition (RRPR 2018), Lecture Notes in Computer Science, Vol. 11455, pp. 138–149 DOI: 10.1007/978-3-030-23987-9 13 Abstract: Robustness is an important concern in machine learning and pattern recognition, and has attracted a lot of attention from technical and scientific viewpoints. Actually, the robustness models the capacity of a computerized approach to resist to perturbing phenomena and data uncertainties, and generate common artefact while designing algorithms. However, this question has not been dealt in depth in such a way for image processing tasks. In this article, we propose a novel definition of robustness dedicated to image processing algorithms. By considering a generalized model of image data uncertainty, we encompass the classic additive Gaussian noise alteration that we study through the evaluation of image denoising algorithms, but also more complex phenomena such as shape variability, which is considered for liver volume segmentation from medical images. Furthermore, we refine our evaluation of robustness wrt. our previous work by introducing a novel quality-scale definition. To do so, we calculate the worst loss of quality for a given algorithm over a set of uncertainty scales, together with the scale where this drop appears. This new approach permits to reveal any algorithm’s weakness, and for which kind of corrupted data it may happen. 13. Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-specific Interactive Re- finement Authors: Ashis Kumar Dhara, Kalyan Ram Ayyalasomayajula, Erik Arvids(1), Markus Fahlstrom(1),¨ Johan Wikstrom(1),¨ Elna-Marie Larsson(1), Robin Strand (1) Division of Radiology, Dept. of Surgical Sciences, UU In Proceedings: 4th International Brain Lesion (BrainLes) workshop, a MICCAI 2018 satellite event. Lec- ture Notes in Computer Science, Vol. 11383 (revised selected papers) pp. 115–122

74 DOI: 10.1007/978-3-030-11723-8 11 Abstract: Accurate volumetric change estimation of glioblastoma is very important for post-surgical treatment follow-up. In this paper, an interactive segmentation method was developed and evaluated with the aim to guide volumetric estimation of glioblastoma. U-Net based fully convolutional network is used for initial segmentation of glioblastoma from post contrast MR images. The max flow algorithm is applied on the probability map of U-Net to update the initial segmentation and the result is displayed to the user for interactive refinement. Network update is performed based on the corrected contour by considering patient specific learning to deal with large context variations among different images. The proposed method is evaluated on a clinical MR image database of 15 glioblastoma patients with longitudinal scan data. The experimental results depict an improvement of segmentation performance due to patient specific fine-tuning. The proposed method is computationally fast and efficient as compared to state-of-the-art interactive segmentation tools. This tool could be useful for post-surgical treatment follow-up with minimal user intervention. 14. Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion Authors: Ekta Vats, Anders Hast, Alicia Fornes(1)´ (1) Universitat Autonoma` de Barcelona In Proceedings: 15th International Conference on Document Analysis and Recognition, pp. 1294–1299 DOI: 10.1109/ICDAR.2019.00209 Abstract: Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. How- ever, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.

6.4 Other 1. Image Analysis in Digital Pathology: Combining Automated Assessment of Ki67 Staining Quality with Calculation of Ki67 Cell Proliferation Index Authors: Ewert Bengtsson, Petter Ranefall Journal: Cytometry, Vol. 95, No. 7, pp. 714–716 DOI: 10.1002/cyto.a.23685 Comment: Commentary 2. Special section on Image Cytometry Authors: Ewert Bengtsson, Attila Tarnok´ (1) (1) Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Germany Journal: Cytometry, Vol. 95:4, pp. 363–365 DOI: 10.1002/cyto.a.23762 Comment: Reviewed invited editorial 3. Hans Radstr˚ om¨ and how to deﬁne smooth functions on any set Author: Christer O. Kiselman Journal: The Bulletin of the Swedish Mathematical Society, May, pp. 20–23 4. Towards automated multiscale Glomeruli detection and analysis in TEM by fusion of CNN and LBP maps Authors: Elisabeth Wetzer, Joakim Lindblad, Ida-Maria Sintorn, Kjell Hultenby(1), Natasaˇ Sladoje (1) Electron Microscopy Core Facility, Karolinska Institute, Huddinge In Proceedings: 3rd NEUBIAS Conference, Luxembourg Comment: Abstract review

75 5. Texture-based oral cancer detection: A performance analysis of deep learning approaches Authors: Jo Gay, Hugo Harlin, Elisabeth Wetzer, Joakim Lindblad, and Natasaˇ Sladoje In Proceedings: 3rd NEUBIAS Conference, Luxembourg Comment: Abstract review 6. Visualization of convolutional neural network class activations in automated oral cancer detection for interpretation of malignancy associated changes Authors: Nadezhda Koriakina, Natasaˇ Sladoje, Ewert Bengtsson, Eva Darai Ramqvist(1), Jan M. Hirsch(2), Christina Runow Stark(3), Joakim Lindblad (1) Pathology and Cytology, Karolinska Institute, Stockholm, Sweden (2) Oral and Maxillofacial Surgery, UU (3) Public Dental Service, Sodersjukhuset,¨ Stockholm, Sweden In Proceedings: 3rd NEUBIAS Conference, Luxembourg Comment: Abstract review 7. CBA Annual Report 2018 Editors: Gunilla Borgefors, Filip Malmberg, Ingela Nystrom,¨ Leslie Solorzano, Robin Strand Publisher: Centre for Image Analysis, 107 pages

76 7 Activities In this section, we list all the “other” work we do, apart from teaching and research. This is not a small part of our activities, and is an important way of keeping in touch with other scientists and also to showcase what we do to the general public. Among the various conferences and workshops we organised (Section 7.1), extra noteworthy is the activity at the Technical Museum in Stockholm where 600 14-year olds were introduced to the work of engineers. We held 28 seminars outside CBA (Section 7.2), the closest at the IT department UU, the farthest at University of Victoria in Canada. We are also really proud of our own long-standing well attended seminar series (Section 7.3), with a CBA seminar almost all Monday afternoons. In 2019, we had 33 seminars, of which 16 were held by external scientists from Sweden or abroad. The average number of attendees was 21, ranging from 12 to 30. As usual, we attended many international and national meetings, where we presented our work as invited speakers or giving oral or poster presentations (Section 7.4). We also spoke at very many smaller, non-reviewed meetings (Section 7.5). In total, CBA people presented 50 times at various meetings, or, if seminars are counted, 95 times, equaling almost two per week. We also attended many conferences just to listen and learn (Section 7.6). In fact, almost everybody participated in the 2019 Swedish Symposium on Image Analysis in Goteborg.¨ In 2019, we had five international visiting scientists that stayed from a week two seven months (Section 7.7). The longest stay was by Guest Professor Fred Hamprecht from Heidelberg University, Germany. We ourselves visited five other labs, again from a few days to up to three months (Section 7.8). A rewarding and necessary part of being an international scientist is serving the scientific community by working for professional organisations, being Editors of scientific journals, serving in program committees for international and national conferences, reviewing for international journals (which often goes undocumented), being members of dissertation committees, and functioning as evaluators of projects and positions. Many of the CBA seniors have many such engagements, which are listed in Section 7.9.

7.1 Conference organisation 1. Special session on Correlated Multimodal Imaging in Life Sciences (COMULIS) Organisers: Natasaˇ Sladoje Address: Glasgow, UK Date: 20190319 Comment: EMIM Molecular Imaging meeting; Co-organiser together with the core group of COST CO- MULIS project.

2. 3rd Swedish Symposium on Deep Learning (SSDL 2019) Organisers: Ida-Maria Sintorn, Robin Strand, Joakim Nivre (1) (1) Dept. of Linguistics and Philology, UU Address: Norrkoping¨ Visualization Center, Norrkoping¨ Date: 20190610–20190611

3. 21st Scandinavian Conference on Image Analysis (SCIA 2019) Organisers: Jonas Unger (1), Ida-Maria Sintorn, Michael Felsberg (2), Per-Erik Forssen´ (2) (1) Dept. of Science and Technology, Linkoping¨ University (2) Dept. of Electrical Engineering, Linkoping¨ University Address: Norrkoping¨ Visualization Center, Norrkoping¨ Date: 20190611–20190613

4. Analysis Day in Memory of Mikael Passare Organisers: Mats Andersson (1), Christer Oscar Kiselman, Pavel Kurasov (2) (main organiser) (1) Dept. of Mathematics at the Chalmers University of Technology and University of Goteborg¨

77 (2) Institute of Mathematics, Stockholm University Address: Stockholm University Date: 20190911 Comment: Annual conference in memory of Mikael Passare (1959–2011).

5. IVA 100 years: Experience, Experiment, Examine – as an Engineer (IVA 100 ar:˚ Upplev, upptack,¨ utforska – som en ingenjor)¨ Organisers: Carolina Wahlby¨ , Ewert Bengtsson, and IVA Address: Tekniska Museet (the Stockholm Museum of Science), Stockholm Date: 20190916–20190917 Comment: This popular science meeting was organised to celebrate the 100th birthday of the Royal Academy of Engineering Sciences, IVA. Six hundred 7th graders were invited to a day of lectures and hands-on experiments guided by PhD students and researchers at Tekniska Museet (TM) in Stockholm. Carolina Wahlby¨ acted as coordinator of the full event, in collaboration with staff from IVA and TM, and Ginevra Castel- lano organised one of the workshops, where the students could interact with robots. Ewert Bengtsson was responsible for coordinating the event-room.

6. Tutorial at XXIV IberoAmerican Congress on Pattern Recognition (CIARP 2019) Organisers: Filip Malmberg, Fredrik Nysjo,¨ Ingela Nystrom¨ Address: Hotel Nacional, Havana, Cuba Date: 20191028 Comment: Arranged a half-day tutorial for the CIARP participants on ”Interactive Segmentation and Visu- alization of Medical Images – from theory to practice”.

7. Swedish Society for Automated Image Analysis workshop Organisers: Ida-Maria Sintorn, Robin Strand, and rest of SSBA board Address: Storgarden,˚ Rimforsa Date: 20191105–20191106

8. COMULIS conference on Multimodal Imaging in Life Sciences Organisers: Natasaˇ Sladoje Address: Natural History Museum of Vienna, Austria Date: 20191121–20191122 Comment: Co-organiser together with the core group of COST COMULIS project.

9. Medical Image Analysis and Medical Engineering: How do we provide solutions for tomorrow’s clinical needs? Organisers: Ingela Nystrom¨ , Fredrik Nikolajeff (1) (1) Dept. of Materials Science and Engineering, UU Address: Noor Castle, Knivsta Date: 20191203–20191204 Comment: Medtech Science & Innovation and CBA arranged this workshop to build networks. The 37 participants presented research activities, brainstormed on joint research interests, funding opportunities, etc.

7.2 Seminars held outside CBA 1. Joakim Lindblad Date: 20190130 Address: CMIV, Linkoping¨ University Title: Image- and AI-based cytological cancer screening Comment: Analytic Imaging Diagnostics Arena (AIDA) meeting.

2. Anna Klemm Date: 20190205 Address: Belval campus of the University of Luxembourg Title: CellProﬁler Comment: Network of European BioImage Analysts (NEUBIAS) Conference - Satellite Meeting.

78 3. Anna Klemm Date: 20190221 Address: BMC, UU Title: Working as a bioimage analyst in a core facility Comment: Fikareers meeting. 4. Gabriele Partel Date: 20190227 Address: University of Torino, Italy Title: In-Situ Sequencing (ISS): placing RNA in context and space Comment: Seminar series in machine learning and life science. 5. Ingela Nystrom¨ Date: 20190304 Address: University of Victoria, Canada Title: Enhanced medical intervention Comment: Guest lecture in the course Medical Image Processing as Distinguished Woman Scholar. 6. Ingela Nystrom¨ Date: 20190305 Address: Dept. of Electrical and Computer Engineering, University of Victoria, Canada Title: Interactive 3D image segmentation and visualization for medical applications Comment: Lunch talk for the Faculty of Engineering at UVic as Distinguished Woman Scholar. 7. Ingela Nystrom¨ Date: 20190306 Address: Dept. of Electrical and Computer Engineering, University of Victoria, Canada Title: Towards equal opportunities in Swedish academia – experiences from and lessons learned at the faculty of science and technology at UU Comment: Lunch talk for the UVic community as Distinguished Woman Scholar. 8. Anindya Gupta Date: 20190315 Address: Karolinska Institutet, Stockholm Title: How deep learning-based methods can be applied to improve the microscopy image analysis of cells and tissue samples Comment: Invited speaker on lecture to health informatics master students. 9. Filip Malmberg, Ingela Nystrom¨ Date: 20190410 Address: BMC, UU Title: 3D geometry and medical image processing Comment: Guest lecture on the master course 3D Printing and Bioprinting in the Life Sciences. 10. Ewert Bengtsson Date: 20190513 Address: Regional Cancer Centre, Thiruvananthapuram, Kerala, India Title: Automated analysis of Pap smear Comment: Indo-Swedish seminar on analytical cell biology and machine learning in cancer research. 11. Joakim Lindblad Date: 20190513 Address: Regional Cancer Centre, Thiruvananthapuram, Kerala, India Title: Image- and AI-based cytological cancer screening Comment: Indo-Swedish seminar on analytical cell biology and machine learning in cancer research. 12. Ida-Maria Sintorn Date: 20190514 Address: BMC, UU Title: Artiﬁcial intelligence and deep learning for biomedical image analysis Comment: Invited to speak at SciLifeLab workshop on artiﬁcial intelligence in the life sciences.

79 13. Nadezhda Koriakina Date: 20190515 Address: Centre for Development of Advanced Computing (C-DAC), Thiruvanantapuram, India Title: Attribution methods for convolutional neural networks 14. Robin Strand Date: 20190523 Address: CMIV, Linkoping¨ University Title: Interactive deep learning segmentation for decision support in neuroradiology Comment: Analytic Imaging Diagnostics Arena (AIDA) mid project presentation. 15. Ingela Nystrom¨ Date: 20190613 Address: UU main building Title: Visualization of medical images Comment: A delegation of university leaders and researchers visited UU on the occasion of the 60-year celebration of bilateral agreement between Sweden and the Republic of Korea. 16. Ingela Nystrom¨ Date: 20190614 Address: Piperska muren, Stockholm Title: Interactive 3D image segmentation and visualization for medical applications – where are we now and where do we go from here Comment: Sweden-Korean Research and Development Networking Day for University leaders, funding agencies, and researchers on the occasion of the 60-year celebration of bilateral agreement between Sweden and the Republic of Korea. Organised jointly by the Swedish Research Council and the national Research Foundation of Korea. 17. Ingela Nystrom¨ Date: 20190630–20190703 Address: UU, Campus Gotland, Visby Title: Almedalsveckan 2019 Comments: Participated in panels and seminars organised by Medtech, Upptech, the Swedish Research Council, and Vetenskap och Allmanhet.¨ Had meetings with UU representatives on Digital Humanities and Medical Engineering. 18. Robin Strand Date: 20190827 Address: Radiology Dept., UU Title: AI i bildanalys, AIDA och andra koncept Comment: Invited presentation at Radiology Dept. Kick-off. 19. Filip Malmberg Date: 20190925 Address: Dept. of Information Technology, UU Title: Optimization of max-norm objective functions in image processing and computer vision 20. Robin Strand Date: 20191108 Address: Radiology Dept., UU hospital Title: Medicinsk bildanalys, big data och artiﬁciell intelligens Comment: Lunch seminar at the Radiology Dept. 21. Anna Klemm Date: 20191111 Address: Biomaterials Research Center, University of Goteborg¨ Title: Biomaterials meets AI 22. Robin Strand Date: 20191111 Address: Angstr˚ om,¨ UU

80 Title: WONDER - Gender WOrk eNvironemnet anD wEllbeing pRoject (Ge-Wonder) Comment: Presentation at network meeting for UU equal opportunities. 23. Robin Strand Date: 20191112 Address: UU main building Title: Medicinsk bildanalys i en era av big data och artificiell intelligens Comment: Professor inauguration lecture. 24. Robin Strand Date: 20191113 Address: SVA (National Veterinary Institute), Uppsala Title: Artificial intelligence and image analysis 25. Carolina Wahlby¨ Date: 20191118 Address: Karolinska Institutet, Stockholm Title: Image analysis and AI as a tool in cancer research Comment: BRECT - Breast Cancer Theme Center symposium 2019. 26. Robin Strand Date: 20191210 Address: KTH Royal Institute of Technology and Karolinska Institutet, Stockholm Title: A whole-body MR image analysis concept, Imiomics, and some of its applications 27. Joakim Lindblad, Nadezhda Koriakina Date: 20191212 Address: CMIV, Linkoping¨ University Title: Image- and AI-based cytological cancer screening 28. Robin Strand Date: 20191212 Address: CMIV, Linkoping¨ University Title: Interactive deep learning segmentation for decision support in neuroradiology Comment: Analytic Imaging Diagnostics Arena (AIDA) final project presentation.

7.3 Seminars at CBA 1. Hanqing Zhang Date: 20190114 Title: Quantitative light microscopy and image processing methods in biological applications 2. Eva Breznik Date: 20190121 Title: Integrating prior knowledge into deep learning 3. Hampus Frojdholm,¨ Hamish Darbyshire, Carmen Lee, Dag Lindgren, Erik Avetisyan, Fredrick Jonasson, Michael Wallgren Fjellander Date: 20190128 Title: Course Computer Assisted Image Analysis I, challenge winner presentations Comment: External presenters. UU master students. 4. Fredrik Nysjo¨ Date: 20190204 Title: What is ray tracing? And how can we teach it to our students? 5. Tuan D. Pham Date: 20190211 Title: Fuzzy recurrence plots and networks Comment: External presenter. Afﬁliation: Dept. of Biomedical Engineering, Linkoping¨ University.

81 30

Figure 43: Our own CBA semniar series. Pink represents seminars given by guests, while blue represent seminars given by CBA people. The darker tops represent guest attendents. The unﬁlled staples represent missing data that are shown as the median value for blue seminars.

6. Raphaela Heil Date: 20190218 Title: Exploration of capsule networks

7. Wei Ouyang Date: 20190225 Title: Differentiable programming for advanced microscopy imaging Comment: External presenter. Afﬁliation: SciLifeLab, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH, Stockholm.

8. Ronald van den Berg Date: 20190304 Title: How to stop worrying and learn to love the Bayes factor Comment: External presenter. Afﬁliation: Dept. of Psychology, UU.

9. Kalyan Ram Ayyalasomayajula Date: 20190311 Title: Recurrent neural model for handwriting generation

10. Anna Klemm Date: 20190401 Title: KNIME - An open-source data analytics platform

11. Anders Hast Date: 20190408

82 Title: A pipeline for efﬁcient visualization and subspace classiﬁcation of handwritten letters and its neural network interpretation

12. Kalyan Ram Ayyalasomayajula Date: 20190429 Title: PhD thesis rehearsal

13. Tomas Wilkinson Date: 20190506 Title: PhD thesis rehearsal

14. Amit Suveer Date: 20190513 Title: PhD thesis rehearsal

15. Shantanu Singh Date: 20190520 Title: How to make a picture worth a thousand numbers: models and methods in biological image analysis Comment: External presenter. Afﬁliation: Imaging Platform, Broad Institute of Harvard and MIT, Boston, USA.

16. Fred Hamprecht Date: 20190603 Title: Probabilistic graphical models and neural networks Comment: External presenter. Afﬁliation: Heidelberg Collaboratory for Image Processing, Heidelberg University, Germany.

17. Tomas Wilkinson Date: 20190604 Title: Learning based word search and visualization for historical manuscript images

18. Amit Suveer Date: 20190605 Title: Methods for processing and analysis of biomedical TEM images

19. Philip Harrison Date: 20190617 Title: Deep dynamical modelling of drug delivery based on microscopy image data Comment: External presenter. Afﬁliation: Dept. of Pharmaceutical Biosciences, UU.

20. MyoungHee Kim Date: 20190828 Title: Biomedical image informatics technology using patient derived heterogeneous big data for precision medicine Comment: External presenter. Afﬁliation: Dept. of Computer Science and Engineering, Ewha Woman’s University, Seoul, South Korea.

21. Pol del Aguila Pla Date: 20190909 Title: Inverse problems in signal processing - cell detection by inverse diffusion Comment: External presenter. Afﬁliation: Div. of Information Science and Engineering, School of Electri- cal Engineering and Computer Science, KTH, Stockholm.

22. Ingela Nystrom¨ Date: 20190916 Title: Towards equal opportunities in academia - experiences and lessons learned internationally and at UU

23. Ankit Gupta Date: 20190930 Title: The HASTE project

83 24. Axel Andersson Date: 20191021 Title: Decoding gene expression

25. Orcun Goksel¨ Date: 20191028 Title: Deep variational networks for sound-speed image reconstruction Comment: External presenter. Afﬁliation: Dept. of Information Technology and Electrical Engineering, ETH Zurich, Switzerland.

26. Punam Saha Date: 20191104 Title: Osteoporosis, bone microarchitecture, and imaging – recent developments and translational studies Comment: External presenter. Afﬁliation: Dept. of Electrical and Computer Engineering and Radiology, University of Iowa, USA.

27. Karl Bengtsson Bernander Date: 20191111 Title: Robust learning of geometric equivariances

28. Nadezhda Koriakina Date: 20191118 Title: Deep learning and explainable artiﬁcial intelligence for biomedical applications

29. Hakan˚ Wieslander Date: 20191118 Title: Efﬁcient analysis of spatial and temporal image data

30. Teo Asplund Date: 20191125 Title: PhD thesis rehearsal

31. Damian Matuszewski Date: 20191202 Title: PhD thesis rehearsal

32. Heikki Kalvi¨ ainen¨ Date: 20191205 Title: Computer vision at LUT University: research and applications Comment: External presenter. Afﬁliation: Computer Vision and Pattern Recognition Laboratory (CVPRL), Dept. of Computational and Process Engineering, School of Engineering Science, LUT University, Finland.

33. Jens Eriksson Date: 20191209 Title: Methods to quantify collective cell migration from bright ﬁeld microscopy data using Python Comment: External presenter. Afﬁliation: Dept. of Medical Biochemistry and Microbiology, UU.

34. Simon Nørrelykke Date: 20191211 Title: Spatial statistics in 3D microscopy Comment: External presenter. Afﬁliation: Scientiﬁc Centre for Optical and Electron Microscopy–ScopeM, ETH Zurich, Switzerland.

35. Peter Horvath Date: 20191211 Title: Cell segmentation - from active contours to learning based approaches in 2D and 3D Comment: External presenter. Afﬁliations: Institute of Molecular Medicine Finland (FIMM); Institute of Biochemistry, Biological Research Center of the Hungarian Academia of Sciences, Szeged.

84 7.4 Conference participation 7.4.1 Special invited speakers International

1. Conference: XXIV IberoAmerican Congress on Pattern Recognition (CIARP 2019) Ingela Nystrom¨ Date: 20191028–20191031 Address: Hotel Nacional, Havana, Cuba Title: Interactive segmentation and visualization of medical images – from theory to practice Comment: Nystrom¨ was one of the General Chairs of the conference. Nystrom,¨ Malmberg, and Nysjo¨ arranged a half-day tutorial for the conference participants. 2. Conference: Sanger Institute Workshop on Machine Learning Carolina Wahlby¨ Date: 20190501–20190502 Address: Sanger Institute, Cambridge, UK Title: In situ sequencing & tissue morphology; should we do segmentation? 3. Conference: Network of European BioImage Analysts (NEUBIAS) Conference Carolina Wahlby¨ Date: 20190202–20190208 Address: Neumunster¨ Abbey, Luxembourg City Title: Analysis of large tissue samples new possibilities with deep learning and spatially resolved analysis of gene expression National

1. Conference: The Annual Swedish Dental Congress and Swedental 2019 (Odontologisk Riksstamma¨ & Swedental 2019) Joakim Lindblad Date: 20191113–20191115 Address: Stockholmsmassan,¨ Alvsj¨ o,¨ Stockholm Title: AI in healthcare (in Swedish) Comment: Invited speaker at the Dialogue Scene at ”Future Lab – Health”. 2. Conference: Research Policy Day 2019 Ingela Nystrom¨ Date: 20191120 Address: Stockholm Waterfront Title: Research infrastructures - digital possibilities and challenges Comment: ”Forskningspolitiska dagen 2019” was arranged by the Swedish Research Council and attracted 300 participants from academia, politics, and companies. Nystrom¨ was panel member in a two-hour discus- sion. 3. Conference: Workshop on Mathematics for Complex Data Natasaˇ Sladoje Date: 20190624 Address: KTH, Stockholm Title: Distance functions for robust image registration: theory and applications 4. Conference: Uppsala Science Slam Carolina Wahlby¨ Date: 20190322 Address: UU Title: Can AI replace a doctor? (in Swedish) Comment: Popular science event at the UU hall for new university students.

85 5. Conference: The Swedish Tumor Microenvironment Meeting 2019 Carolina Wahlby¨ Date: 20190409–20190411 Address: Umea˚ Title: ”Seeing is believing, measuring is knowing” - quantitative analysis in digital pathology

6. Conference: Swedish National Infrastructure for Computing (SNIC) All Hands 2019 Ingela Nystrom¨ Date: 20190905–20190906 Address: Rosersbergs Castle, Stockholm Title: The Council for Research Infrastructure (RFI) - a decision body of the Swedish Research Council (SRC)

7. Conference: New Horizons in Diabetes, Uppsala Robin Strand Date: 20190912–20190913 Address: Centre for the humanities, UU Title: Imiomics and machine learning in whole-body imaging studies of diabetes Comment: https://www.medfarm.uu.se/newhorizons2019/

8. Conference: 12th International Workshop on Approaches to Single Cell Analysis Carolina Wahlby¨ Date: 20190304–20190305 Address: UU main building Title: Single cell analysis by digital image processing of tissue and time-lapse data

9. Conference: Engineering Health 2019 Carolina Wahlby¨ Date: 20190320–20190321 Address: Wallenberg conference centre, Goteborg¨ Title: AI in digital pathology and biomedicine - potential and limitations

7.4.2 Refereed conference presentations 1. Conference: Italian Research Conference on Digital Libraries - Digital Libraries: Supporting Open Science (IRCDL 2019) Anders Hast Date: 20190131–20190201 Address: Pisa, Italy Title: Making large collections of handwritten material easily accessible and searchable

2. Conference: Digital Humanities in the Nordic Countries (DHN) Raphaela Heil Date: 20190306–20190308 Address: Copenhagen, Denmark Title: Creating an atlas over handwritten script signs

3. Conference: 21th International Conference on Discrete Geometry for Computer Imagery (DGCI) Johan Ofverstedt¨ Date: 20190325–20190329 Address: ESIEE Paris, France Title: Stochastic distance transform

4. Conference: 21th International Conference on Discrete Geometry for Computer Imagery (DGCI) Filip Malmberg Date: 20190325–20190329 Address: ESIEE Paris, France Title: Optimization of max-norm objective functions in image processing and computer vision

86 5. Conference: IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Anindya Gupta Date: 20190408–20190411 Address: Venice, Italy Title: Super-resolution reconstruction of transmission electron microscopy images using deep learning 6. Conference: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing (ISMM 2019) Teo Asplund Date: 20190708–20190710 Address: Saarland University, Saarbrucken,¨ Germany Title: Mathematical morphology on irregularly sampled data applied to segmentation of 3D point clouds of urban scenes 7. Conference: 18th International Conference (CAIP 2019) Anders Hast Date: 20190903–20190905 Address: Salerno, Italy Title: Embedded prototype subspace classiﬁcation : a subspace learning framework 8. Conference: 15th IEEE Conference on Signal Image Technology and Internet based Systems Anders Hast Date: 20191126–20191129 Address: Sorrento-Naples, Italy Title: Face recognition - A one-shot learning perspective 9. Conference: 5th Asian Conference on Pattern Recognition (ACPR 2019) Sukalpa Chanda Date: 20191126–20191129 Address: Auckland, New Zealand Title: Finding logo and seal in historical document images - An object detection based approach

7.5 Non-refereed conference presentations 1. Conference: Mathematics in Imaging Elisabeth Wetzer Date: 20190107–20190111 Address: Centre International de Rencontres Mathematiques,´ Marseille, France Title: Towards automated multiscale imaging and analysis in TEM 2. Conference: Network of European BioImage Analysts (NEUBIAS) Conference Joakim Lindblad Date: 20190202–20190208 Address: Neumunster¨ Abbey, Luxembourg City Title: Texture-based oral cancer detection: a performance analysis of deep learning approaches Comment: Workshops and Conference. 3. Conference: Network of European BioImage Analysts (NEUBIAS) Conference Natasaˇ Sladoje Date: 20190202–20190208 Address: Neumunster¨ Abbey, Luxembourg City Title: EDAM-bioimaging: the ontology of bioimage informatics operations, topics, data, and formats (2019 update) Comment: Workshops and Conference. 4. Conference: Network of European BioImage Analysts (NEUBIAS) Conference Nadezhda Koriakina Date: 20190206–20190208 Address: Neumunster¨ Abbey, Luxembourg City

87 Title: Visualization of convolutional neural network class activations in automated oral cancer detection for interpretation of malignancy associated changes 5. Conference: Network of European BioImage Analysts (NEUBIAS) Conference Elisabeth Wetzer Date: 20190206–20190208 Address: Neumunster¨ Abbey, Luxembourg City Title: Towards automated multiscale glomeruli detection and analysis in TEM by fusion of CNN and LBP maps Comment: Also two posters, one on the same topic, one on ”Texture-based oral cancer detection: a performance analysis of deep learning approaches”. 6. Conference: 37th Annual Swedish Symposium on Image Analysis (SSBA 2019) Hanqing Zhang Date: 20190318–20190320 Address: Lindholmen Conference Center, Goteborg¨ Title: Automated screening in zebraﬁsh using a customized optical projection tomography system Comment: A conference paper was submitted. 7. Conference: 37th Annual Swedish Symposium on Image Analysis (SSBA 2019) Johan Ofverstedt¨ Date: 20190318–20190320 Address: Lindholmen Conference Center, Goteborg¨ Title: Robust symmetric afﬁne image registration / stochastic distance functions with applications in object detection and image segmentation Comment: Two talks given. 8. Conference: 37th Annual Swedish Symposium on Image Analysis (SSBA 2019) Elisabeth Wetzer Date: 20190319–20190320 Address: Lindholmen Conference Center, Goteborg¨ Title: Oral cancer detection: a comparison of texture focused deep learning approaches 9. Conference: National Bioinformatics Infrastructure Sweden (NBIS) retreat Petter Ranefall Date: 20190410 Address: Haga Slott, Enkoping¨ Title: BioImage informatics facility Comment: + Poster. 10. Conference: SciLifeLab Science Summit Carolina Wahlby,¨ Anindya Gupta Date: 20190515 Address: UKK, Uppsala Title: Automated prediction of mesenchymal cell migration modes in confocal microscopy images using deep learning 11. Conference: SciLifeLab Mini Symposium Anna Klemm Date: 20190523 Address: Solna Title: SciLifeLab BioImage Informatics Facility 12. Conference: ELRIG 2019 (European Laboratory Research &Innovation Group) – Advances in Cell Based Screening in Drug Discovery Carolina Wahlby¨ Date: 20190522–20190524 Address: Goteborg¨ Title: Intro: Multiplexed screening with single cell resolution Comment: Carolina Wahlby¨ organised the meeting together with a local committee at Astra Zeneca, and introduced the session on Multiplexed screening with single cell resolution.

88 13. Conference: UPPTECH alumn event Carolina Wahlby¨ Date: 20190611 Address: Stockholm Title: Den digitala framtiden-AI i mansklighetens¨ tjanst¨ Comment: A popular science alumn event organised by UPPTECH - an initiative to promote science and technology at UU. 14. Conference: 3rd Swedish Symposium on Deep Learning (SSDL 2019) Teo Asplund, Eva Breznik Date: 20190610–20190611 Address: Norrkopings¨ Visualiseringscenter C, Norrkoping¨ Title: CNNs on graphs: a new pooling approach and similarities to mathematical morphology 15. Conference: Phenotarget community meeting Carolina Wahlby¨ Date: 20190613 Address: Stockholm Title: The microscope as an automated measurement tool Comment: The first annual meeting of a national community focused on phenotypic drug screening. 16. Conference: Phenotarget community meeting Hanqing Zhang Date: 20190613 Address: Stockholm Title: Automated screening in zebrafish using a customized optical projection tomography system 17. Conference: Eastern European Machine Learning Summer School (EEML) Eva Breznik Date: 20190701–20190706 Address: Bucharest, Romania Title: Abdominal organ segmentation in whole-body MRI Comment: Summer school, with poster presentations one evening. 18. Conference: European Congress and Exhibition on Advanced Materials and Processes (EUROMAT) Ida-Maria Sintorn Date: 20190901–20190905 Address: Stockholm City Conference Centre, Stockholm Title: Imaging and characterization of inorganic and biological nanoparticles and fibres in MiniTEM Comment: E-poster presentation. 19. Conference: Uppsala Zebrafish Forum workshop Hanqing Zhang Date: 20190927 Address: EBC, UU Title: Zebrafish Image Informatics (ZII) 20. Conference: Uppsala Zebrafish Forum workshop Amin Allalou Date: 20190927 Address: EBC, UU Title: Zebrafish Image Informatics (ZII) – services built on the vertebrate automated screening technology 21. Conference: SciLifeLab Facility Forum Amin Allalou Date: 20190930–20191001 Address: Djuron¨ aset,¨ Varmd¨ o,¨ Djurhamn Title: High-throughput screening in live zebrafish 22. Conference: ViB Conference: The Brain Mosaic 2019 Gabriele Partel

89 Date: 20191010–20191011 Address: Leuven, Belgium Title: High resolution spatial gene expression maps for unsupervised brain compartment annoation Comment: Federation of European Biochemical Societies (FEBS) Letters Poster Prize awarded. 23. Conference: Network of European BioImage Analysts (NEUBIAS) TS13 Training School for Facility Staff Anna Klemm Date: 20191014–20191017 Address: Porto, Portugal Title: Advanced ImageJ macro programming 24. Conference: Institute for Molecular Medicine Finland (FIMM) High Content Screening Symposium Carolina Wahlby¨ Date: 20191021 Address: Helsinki, Finland Title: In situ sequencing & tissue morphology; from images to compartments and cells 25. Conference: Women in Computational Biology Carolina Wahlby¨ Date: 20191111–20191113 Address: Howard Hughes Medical Institute (HHMI) Janelia, Washington DC, USA Title: BioImage Analysis Comment: Carolina Wahlby¨ was also part of organising this ﬁrst meeting with focus on female researchers in computational biology. 26. Conference: Frontiers in Engineering Ida-Maria Sintorn Date: 20191117–20191120 Address: The Royal Swedish Academy of Engineering Sciences, Stockholm Title: AI and IA for automated microscopy 27. Conference: InFuturum Carolina Wahlby¨ Date: 20191128–20191129 Address: Goteborg¨ Title: AI applications in university health and academia - prostate cancer (in Swedish) Comment: A meeting for the leaders of medical faculties and university hospitals in Sweden. 28. Conference: MedTech-CBA workshop Ewert Bengtsson Date: 20191203–20191204 Address: Noors Conference Castle, Knivsta Title: Reﬂections on the past, the present and the future 29. Conference: German–Swedish Innovation Partnership for eHealth - “Joining Forces - European AI in Health” Robin Strand Date: 20191218 Address: Swedish Ministry of Health, Stockholm Title: AI and image processing

7.6 Attended conferences 1. Conference: Wallenberg AI, Autonomous Systems and Software Program (WASP) Winter Conference 2019 Karl Bengtsson Bernander, Joakim Lindblad, Natasaˇ Sladoje Date: 20190115–20190117 Address: Goteborg¨ 2. Conference: Network of European BioImage Analysts (NEUBIAS) workshop Joakim Lindblad

90 Date: 20190121–20190122 Address: Porto, Portugal Comment: Management committee and work-group meetings. 3. Conference: Analytic Imaging Diagnostics Arena (AIDA) Days: AI Applications Today and Future Chal- lenges Ewert Bengtsson Date: 20190129–20190130 Address: CMIV, Linkoping¨ Comment: Workshop organised by the Analytic Imaging Diagnostics Arena (AIDA) program. 4. Conference: Variational Methods and Optimization in Imaging Elisabeth Wetzer Date: 20190204–20190208 Address: Institue Henri Poincare,´ Paris, France 5. Conference: Network of European BioImage Analysts (NEUBIAS) Conference Anna Klemm Date: 20190206–20190208 Address: Luxembourg City 6. Conference: Statistical Modeling for Shapes and Imaging Elisabeth Wetzer Date: 20190311–20190315 Address: Institue Henri Poincare,´ Paris, France 7. Conference: EMIM Special session on COMULIS Correlated Multimodal Imaging in Life Sciences Joakim Lindblad, Natasaˇ Sladoje Date: 20190319 Address: Glasgow, UK 8. Conference: 37th Annual Swedish Symposium on Image Analysis (SSBA 2019) Axel Andersson, Teo Asplund, Ewert Bengtsson, Karl Bengtsson Bernander, Eva Breznik, Anindya Gupta, Ankit Gupta, Raphaela Heil, Anna Klemm, Nadezhda Koriakina, Ingela Nystrom,¨ Gabriele Partel, Nicolas Pielawski, Petter Ranefall, Ida-Maria Sintorn, Leslie Solorzano, Robin Strand, Hakan˚ Wieslander, Carolina Wahlby¨ Date: 20190319–20190320 Address: Lindholmen Conference Center, Goteborg¨ Comment: CBA had 20 participants at the Swedish Symposium on Image Analysis this year. Sintorn as Chair and Strand as Vice-Chair on the SSBA board 2018–2020. Several CBA presentations and PhD students as session chairs. There were approximately 100 participants from academia and industry who gathered for a PhD student day and two symposium days. The interest from non-academic organisations continues. 9. Conference: 21th International Conference on Discrete Geometry for Computer Imagery (DGCI) Christer Oscar Kiselman Date: 20190325–20190328 Address: ESIEE Paris, France Comment: Including conference in honor of Gilles Bertrand. 10. Conference: Is research Useful? Gunilla Borgefors Date: 20190327 Address: Parliament building, Stockholm Comment: Annual meeting of Rifo (Parliamentarians and Scientists) and conference. 11. Conference: Imaging and machine learning Elisabeth Wetzer Date: 20190401–20190405 Address: Institue Henri Poincare,´ Paris, France

91 12. Conference: IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Joakim Lindblad, Natasaˇ Sladoje Date: 20190408–20190411 Address: Venice, Italy 13. Conference: STorM Axel Andersson Date: 20190409–20190411 Address: UmeaFolkets˚ Hus, Umea˚ Comment: The Swedish Tumor Microenvironment Meeting 2019. 14. Conference: Cerviscan 10th workshop Ewert Bengtsson, Nadezhda Koriakina, Joakim Lindblad, Christina Runow Stark Date: 20190514–20190515 Address: Centre for Development of Advanced Computing (C-DAC), Thiruvanantapuram, India Comment: Part of a 10 year long series of collaboration workshops CBA-C-DAC/RCC in India. 15. Conference: SciLifeLab Science Summit Anna Klemm, Petter Ranefall Date: 20190515 Address: UKK, Uppsala 16. Conference: Analytic Imaging Diagnostics Arena (AIDA) Days: AIDA project presentations Joakim Lindblad Date: 20190522–20190523 Address: CMIV, Linkoping¨ Comment: Workshop organised by the AIDA program. 17. Conference: ELRIG Advances in Cell Based Screening in Drug Discovery Ankit Gupta Date: 20190522–20190524 Address: Goteborg¨ 18. Conference: 3rd Swedish Symposium on Deep Learning (SSDL 2019) Axel Andersson, Ashis Kumar Dhara, Isabelle Enlund, Ankit Gupta, Anders Hast, Ingela Nystrom,¨ Ida-Maria Sintorn, Robin Strand, Tomas Wilkinson Date: 20190610–20190611 Address: Norrkoping¨ Visualization Center C, Linkoping¨ University, Norrkoping¨ 19. Conference: 21st Scandinavian Conference on Image Analysis (SCIA 2019) Axel Andersson, Ewert Bengtsson, Gunilla Borgefors, Eva Breznik, Ashis Kumar Dhara, Ankit Gupta, Ingela Nystrom,¨ Ida-Maria Sintorn, Robin Strand, Johan Ofverstedt¨ Date: 20190611–20190613 Address: Norrkoping¨ Visualization Center C, Linkoping¨ University, Norrkoping¨ Comment: Nystrom¨ chaired the session on Medical and Biomedical Image Analysis. Borgefors and Bengts- son chaired sessions as well. 20. Conference: Scandem Anna Klemm Date: 20190611–20190614 Address: Goteborg¨ 21. Conference: PhenoTarget RCP Petter Ranefall Date: 20190613 Address: BioClinicum, Karolinska Hospital, Solna 22. Conference: Conference on Computer Vision and Pattern Recognition (CVPR) Joakim Lindblad, Natasaˇ Sladoje Date: 20190616–20190620 Address: Long Beach Convention & Entertainment Center, Long Beach, California, USA

92 23. Conference: 14th International Symposium for Mathematical Morphology (ISMM) Christer Oscar Kiselman Date: 20190708–20190711 Address: Saarbrucken,¨ Germany 24. Conference: Society day, Royal Society of Sciences at Uppsala Gunilla Borgefors Date: 20190903 Address: UU Comment: Lectures by this year’s prize winners. 25. Conference: Analysis Day in Memory of Mikael Passare Christer Oscar Kiselman Date: 20190911 Address: Stockholm University 26. Conference: SciLifeLab Science Facility Forum Petter Ranefall, Anna Klemm Date: 20190930–20191001 Address: Djuron¨ aset¨ Konferens & Hotell, Djurhamn 27. Conference: WONDER (WOrk eNvironment aND wEllbeing) retreat about stress management for Vi2 Axel Andersson, Teo Asplund, Ewert Bengtsson, Eva Breznik, Anindya Gupta, Ankit Gupta, An- ders Hast, Raphaela Heil, Anna Klemm, Nadezhda Koriakina, Joakim Lindblad, Ingela Nystrom,¨ Gabriele Partel, Petter Ranefall, Ida-Maria Sintorn, Natasaˇ Sladoje, Robin Strand, Hakan˚ Wieslan- der, Carolina Wahlby,¨ Johan Ofverstedt¨ Date: 20191007–20191008 Address: Krusenberg herrgard,˚ Knivsta 28. Conference: Network of European BioImage Analysts-Correlated Multimodal Imaging in Life Sciences (NEUBIAS-COMULIS) workshop Joakim Lindblad, Natasaˇ Sladoje Date: 20191014–20191017 Address: Porto, Portugal 29. Conference: 100th anniversary of the Royal Academy of Engineering Sciences (IVA) Gunilla Borgefors Date: 20191025 Address: Karolinska Institutet, Stockholm 30. Conference: CytoData Axel Andersson, Ankit Gupta, Nicolas Pielawski Date: 20191027–20191030 Address: German Cancer research Center (DKFZ), Heidelberg, Germany Comment: CytoData Symposium and Hackathon. 31. Conference: SSBA 2019 workshop Ida-Maria Sintorn, Natasaˇ Sladoje, Robin Strand Date: 20191105–20191106 Address: Linkoping¨ University 32. Conference: Analytic Imaging Diagnostics Arena (AIDA) Days: Deep Dive in AI Tech Pool Joakim Lindblad Date: 20191106–20191107 Address: CMIV, Linkoping¨ Comment: Workshop organised by the AIDA program. 33. Conference: Astronomy Conference of the Swedish Astronomical Society Christer Oscar Kiselman Date: 20191126 Address: AlbaNova, Stockholm University

93 34. Conference: Analytic Imaging Diagnostics Arena (AIDA) Days: AI in Clinical Practice Ewert Bengtsson Date: 20191211–20191212 Address: CMIV, Linkoping¨ Comment: Workshop organised by the AIDA program.

7.7 Visiting scientists 1. Carla Pereira Address: University of Porto, Portugal Host: Carolina Wahlby¨ Date: 20190201–20190328 Topic: Guest PhD student 2. Fred Hamprecht Address: Heidelberg Collaboratory for Image Processing, Heidelberg University, Germany Host: Carolina Wahlby¨ Date: 20190201–20190830 Topic: Sabbatical 3. Shantanu Singh Address: Imaging Platform, Broad Institute of Harvard and MIT, Boston, USA Host: Carolina Wahlby¨ Date: 20190518–20190524 Topic: Seminar and research discussions Comments: Also participated in an ELRIG conference at Astra Zeneca in Goteborg.¨ 4. MyoungHee Kim Address: Womens University, Seoul, South Korea Host: Carolina Wahlby¨ Date: 20190826–20190830 Topic: Seminar and research discussions 5. Punam Saha Address: College of Engineering and Iowa Institute for Biomedical Imaging, University of Iowa, USA Host: Robin Strand, Gunilla Borgefors Date: 20191016–20191114 Topic: The Book Comments: Saha, Strand, and Borgefors are writing a book on Digital Geometry together.

7.8 Visits to other research groups 1. Ingela Nystrom¨ Host: Alexandra Branzan-Albu Address: Dept. of Electrical and Computer Engineering, University of Victoria, Canada Date: 20190304–20190308 Topic: Distinguished Women Scholar 2019 at UVic Comments: The visit held three lectures, several meetings at research labs as well as with PhD students and colleagues at UVic. 2. Christer Oscar Kiselman Host: Lawrence Gruman, Ahmed Zeriahi Address: University Paul Sabatier, Toulouse, France Date: 20190321–20190324 Topic: Mathematics 3. Gabriele Partel Host: Rafael Gomez Address: Chan Zuckerberg biohub-an independent nonproﬁt research center, San Francisco, USA

94 Date: 20190501–20190731 Topic: Research visit Comments: Visit founded by awarded Sederholm travel scholarship. 4. Ewert Bengtsson, Joakim Lindblad, Nadezhda Koriakina, Christina Runow Stark Host: Dr. K. Sujathan Address: Regional Cancer Centre (RCC) and Centre for Development of Advanced Computing (C-DAC), Thiruvanantapuram, India Date: 20190509–20190517 Topic: Analytical cell biology and machine learning in cancer research Comments: Research visit funded by Swedish Research Links project 2015-05878 - ”Image analysis for reliable and cost effective cancer detection”. 5. Karl Bengtsson Bernander Host: Max Planck Institutes, Universities of Darmstadt and Aachen Address: Cities Stuttgart, Tubingen,¨ Aachen, Darmstadt in Germany Date: 20191014–20191018 Topic: Wallenberg AI, Autonomous Systems and Software Program (WASP) study trip to Germany Comments: Participation by the WASP AI graduate school.

95 7.9 Committees Ewert Bengtsson International: • Lifetime Fellow of the Institute of Electrical and Electronics Engineers (IEEE), 2015– Comment: Member since 1974. • Member of the International Society for Optical Engineering (SPIE), 2004– • Member of the International Society for Analytical Cytology (ISAC), 2000– • Editorial Board member of Computer Methods and Programs in Biomedicine, 1995—April 2019 Comment: Published by Elsevier. • Editorial Board member of Machine Graphics & Vision, 1994– Comment: Published by the Polish Academy of Sciences. • Associate Editor of Journal of Multimedia Information System, 2014– Comment: Published by Korea Multimedia Society. • Expert for evaluating candidates for a position as Associate Professor in Image Analysis and Pattern Recog- nition/Machine Learning at Dept. of of Informatics at University of Oslo, Norway. • Evaluator of PhD thesis of Mrs Ratna Saha at College of Science and Engineering at Flinders University, Australia. • Evaluator of self-appraisal report for promotion to Associate Professor II for Dr. Lipi B Mahanta at Institute of Advanced Study in Science and Technology, An autonomous institute under DST, Govt. of India, Assam, India National: • Member of the Royal Swedish Academy of Engineering Sciences (IVA), 2006– Comment: Division VII: Basic and Interdisciplinary Engineering Sciences. • Member of the Royal Society of Sciences in Uppsala (Kungliga Vetenskaps-Societeten), 1998– Comment: Elected member of the oldest scientific society in Sweden (founded 1710). • Member of scientific board of Swedish Association for Medical Engineering and Physics, “Svensk forening¨ for¨ medicinsk teknik och fysik” 2013- • Member of scientific board of Hillevi Fries Research Scholarship Foundation, 2006– Comment: A Swedish foundation that accepts applications and gives out research grants for urology research. • Evaluator at half time thesis review for Effie Chantzi at Dept. of of Medical Sciences, Uppsala University. Gunilla Borgefors International: • Fellow of the International Association for Pattern Recognition (IAPR), 1998– Comment: 1st Vice President 1994–96, Secretary 1990–94, etc., etc. • Member of the King Sun Fu Prize committee of the International Association for Pattern Recognition (IAPR), 2016-2022 • Fellow of the Institute of Electrical and Electronics Engineers, Inc. (IEEE), 2007 Comment: Senior member 1998. • Editor Emerita of Pattern Recognition Letters, 2019– Comment: Published by Elsevier. PRL is an official journal of the International Association of Pattern Recognition. Borgefors was Associate/Area Editor 2004–2010 and Editor-in-Chief 2010-2018. • Editorial Board member of Image Processing and Communications, 1994– Comment: Published by the Institute of Telecommunications, Bydgoszcz, Poland.

96 • Editorial Board member of Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, 1993– Comment: Published by Interperiodica Publishing in cooperation with the Russian Academy of Sciences. • Editorial Board of the book series Computational Imaging and Vision, 2003– Comment: Published by Springer. • Steering committee for Discrete Geometry for Computer Imagery (DGCI) conferences, 2000– Programme committee 21th International Conference on Discrete Geometry for Computer Imagery (DGCI 2019), Marne-la-Valle´ (Paris), France, March 2019 • Steering committee for International Symposium on Mathematical Morphology (ISMM), 2011– Programme committee, 14th International Symposium on Mathematical Morphology (ISMM 2019), Saarbrucken,¨ Germany, July 2019 • Programme committee, 14th International Conference on Pattern Recognition and Information Processing (PRIP 19) Minsk, Belarus, May 2019 • Programme committee, 21st Scandinavian Conference on Image Analysis (SCIA 2019), Norrkoping,¨ Swe- den, June 2019 • Programme committee, 4th International Workshop on AI aspects in Reasoning, Languages, and Computa- tion (AIRLangComp’19), Leipzig, Germany, September, 2019 • Programme committee, 18th International Conference on Computer Analysis of Images and Patterns (CAIP 2019), Salerno, Italy, September 2019 • Programme committee, 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, October 2019 • Programme committee, 5th Asian Conference on Pattern Recognition (ACPR 2019), Auckland, New Zealand, November 2019 • Examiner of the Thesis of Aditya Challa, Computer Vision & Pattern Recognition Unit (CVPRU), School of Engineering Science, Indian Statistical Institute, Kolkata, India, October 2019. Comment: Title: Some Studies on Mathematical Morphology for Unsupervised and Semi-Supervised Learning. • ”Rapporteaur” (reporter) of the Thesis of Deise Santana Maia, ESIEE, Cite´ Descartes, Noisy-le-Grand (Paris), France, 20191210. Comment: Title: A study of hierarchical watersheds on graphs with applications to image segmentation. National:

• Member of the Royal Swedish Academy of Engineering Sciences (IVA), 2011– Comment: Division VII: Basic and Interdisciplinary Engineering Sciences. • Member No. 19 of the Royal Society of Sciences in Uppsala (Kungliga Vetenskaps–Societeten), 2000– Comment: Elected member of the oldest scientiﬁc society in Sweden (founded 1710). • Member of Swedish Parliamentarians and Scientists, 1987– Comment: Members are elected. Only one scientist per ﬁeld admitted. • Member of the Board/Steering Committee for Onsala Space Observatory, 2011– Comment: Two physical and several telephone meetings per year.

Christer O. Kiselman International: • American Mathematical Society (life member) • Societ´ e´ Mathematique´ de France • International Academy of Sciences, San Marino • Internacia Scienca Akademio Comenius

97 • European Mathematical Society • Polska Akademia Umiejetnosci´ (Polish Academy of Arts and Sciences) • Associate Member, Scandinavian Society for Iranian Studies National: • Royal Academy of Arts and Sciences, Uppsala, 1983– . • Royal Society of Sciences (Kungliga Vetenskaps-Societeten), Uppsala, 1984– . • Royal Swedish Academy of Sciences, 1990– . • Swedish Astronomical Society (life member) • Swedish Mathematical Society (life member) Joakim Lindblad International: • Management committe member substitute, NEUBIAS – A new Network of European BioImage Analysts to advance life science imaging, 20160503–20200502 Comment: EU COST Action CA 15124 • Program committee member, CTIC Workshop on Computational Topology in Image Context. • Program committee member, IWCIA - International Workshop on Combinatorial Image Analysis. • Management committee member substitute, COMULIS - Correlated Multimodal Imaging in Life Sciences. Comment: EU COST Action CA 17121 - European Cooperation in Science and Technology • Program committee, International Symposium on Mathematical Morphology (ISMM) 2019 National: • Steering board member - International Master program in Image Analysis and Machine Learning. • Ph.D. thesis committee for Jonathan Andersson, Dept. of Surgical Sciences, UU, 20190913 Comment: Thesis title: ”Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue”

Filip Malmberg International: • Deputy editor of Pattern Recognition Letters, 2015– Comment: Published by Elsevier. PRL is an ofﬁcial journal of the International Association of Pattern Recognition. • Program committee member, 24th Iberoamerican Conference on Pattern Recognition (CIARP 2019), Ha- vana, Cuba, 2019. • Program committee member, Medical Image Understanding and Analysis (MIUA 2019), Liverpool, UK • Program committee member, 14th International Symposium on Mathematical Morphology (ISMM 2019), Saarbucken,¨ Germany, 2019. • Ph.D. thesis committee for Kaiwen Chang, 20191210 Comment: Thesis title: Machine learning based image segmentation National • Steering board member - International Master program in Image Analysis and Machine Learning Ingela Nystrom¨ International: • International Association for Pattern Recognition (IAPR)

98 – Governing Board member 2002–2010 – Executive Committee member 2008–2018: 2nd Vice-President 2008–2010, Secretary 2010–2014, President 2014–2016, Past President 2016–2018 – Nominating Committee: member 2004–2006, chair 2016–2018, member 2018–2020 – Constitutions and Bylaws Committee: chair 2018–2020 • Board Member of of UNINETT Sigma2 AS (Infrastructure for Computational Science in Norway) 2019– • General Co-Chair, XXIV IberoAmerican Congress on Pattern Recognition (CIARP 2019), Havana, Cuba, Oct 2019 • Program Committee, 14th International Conference on Pattern Recognition and Image Processing (PRIP 2019), Minsk, Belarus, May 2019 • Program Committee, 21st Scandinavian Conference on Image Analysis (SCIA 2019), Norrkoping,¨ Sweden, Jun 2019 National: • Member of the Royal Society of Arts and Sciences of Uppsala (Kungliga Vetenskapssamhallet¨ i Uppsala), 2012– Comment: Board member 2016–, 4 meetings per year • Member of the Council for Research Infrastructure (RFI), the Swedish Research Council, 2014–2019 Comment: Vice-Chair 2015–2019, meetings approximately 10 days per year • Deputy Member of the Board of Misconduct in Research (“Namnden¨ for¨ utredning av oredlighet i forsk- ning”) at UU, 2017–2019, 8 meetings per year • Member of the Steering Group for the Digital Humanities Uppsala research network, 2018–, 6 meetings per year • Member of Advisory Committee forming WoMHeR - Uppsala Center for Women’s Mental Health during the Reproductive Lifespan, 2018–2019, 6 meetings per year • Mentor in the TekNat Mentorship Programmes: 2014–2015, 2016–2017, 2018–2019 • Auscultation for participants in the course “Supervising PhD students”, 2015–, approximately 4 times per year • External evaluator of position as Associate Professor in Media Technology with Specialisation in Image Analysis and Signal Processing at Umea˚ University, 2019 Petter Ranefall National: • PhD halftime committee, 20191024 Comment: Halftime seminar by Casper Winsnes, supervised by Emma Lundberg, SciLifeLab, Solna.

Stefan Seipel National: • Board of UpGIS, the network of Geographical Information Systems at UU, 2013– Ida-Maria Sintorn International: • Program chair and award committee member, Scandinavian Conference on Image Analysis, organising committee, 20190611–20190613 • Assessment committee for Associate Professor recruitment at Danish Technical University, 20190901– 20191031 • Evaluation committee for grant application to the Vienna Science and Technology Fund (WWTF) Life Sciences Programme, 20190923–20191021

99 National: • Chair of the Swedish Society for Automated Analysis (SSBA), 2018– Comment: Board member since 2008 • Steering board member - International Master program in Image Analysis and Machine Learning. • Vi2 division representative in the Collaborations Group at Dept. IT, UU, 20180101–20180831. • Program chair, Swedish Symposium on Deep Learning organising committee, 20190610–20190611 • PhD thesis grading committee, Diana Telessemian Mahdessian, KTH Royal Institute of Technology, 20191025– 20191025 Comment: Thesis title: “Spatiotemporal characterization of the human proteome” Natasaˇ Sladoje International: • Management committe member, NEUBIAS - A new Network of European BioImage Analysts to advance life science imaging, 20160503–20200502 Comment: COST Action CA 15124 • Management committe member, COMULIS - Correlated Multimodal Imaging in Life Sciences, 20181012– 20221011 Comment: EU COST Action CA 17121 - European Cooperation in Science and Technology • External evaluator of position as researcher in medical image analysis with an inovative focus, 20190101 Comment: Technical University of Denmark (DTU) National: • Program committee, Scandinavian Conference on Image Analysis (SCIA) • Steering board member - International Master program in Image Analysis and Machine Learning. • Program responsible and Program board chair - International Master program in Image Analysis and Ma- chine Learning, 20191101– • Steering board member - Civilingenjorsprogrammet¨ Industriell ekonomi, 2018–2019 • Steering board member - International Master program in Data Science, 2018–2019 • Deputy Board Member - Centre for Interdiciplinary Mathematics, 20190701–20191231 • Ph.D. thesis committee for Bertil Grelsson, Computer Vision Laboratory, Dept. of of Electrical Engineering, Linkoping¨ University, 20190426 Comment: Thesis title: “Vision-based Localization and Attitude Estimation Methods in Natural Environ- ments” • Ph.D. thesis committee for Elin Lundstrom,¨ Dept. of of Surgical Sciences, Radiology, Uppsala University Hospital, 20190607 Comment: Thesis title: “Magnetic Resonance Imaging of Human Brown Adipose Tissue, Methodological Development and Application” Robin Strand International: • Program Committee, Image and Pattern Analysis for Multidisciplinary Computational Anatomy (ipaMCA 2019), Auckland, New Zealand (in conjunction to 5th Asian Conference on Pattern Recognition) • Program Committee, Medical Image Understanding and Analysis (MIUA 2019), Liverpool, UK • Program Committee, Scandinavian Conference on Image Analysis (SCIA 2019), Norrkoping,¨ Sweden (reviewer) • Program Committee, 21st International Conference on Discrete Geometry for Computer Imagery (DGCI 2019), Paris, France

100 National: • Vice Chair, Swedish Society for Automated Image Analysis, 2018– • Dissertation committee for Johanna Nilsson, April 12 2019, Dept. of Surgical Sciences, UU, 2019-04-12 Comment: Title: On Virtual Surgical Planning in Cranio-Maxillofacial Surgery • Half-time control committee for Irene Brusini, KTH Royal Institute of Technology and Karolinska Institute, Stockholm, 2019-12-10 Comment: Title: Methods for the analysis and characterization of brain morphology from MRI images • Election committee, Dept. of IT. Deputy member Carolina Wahlby¨ International: • Management Committee for NEUBIAS Comment: A Network of European BioImage Analysts to advance life science imaging (NEUBIAS), fully funded by COST, a European cooperation in Science and Technology. • PhD thesis opponent, 20190109 Comment: PhD of Scott Warchal, ”Image Informatics Approaches to Advanced Cancer Drug Discovery”, the University of Edinburgh, UK

National: • Member of the Royal Swedish Academy of Engineering Sciences (IVA) • Member of the Royal Society of Sciences in Uppsala (Kungliga Vetenskaps-Societeten) • Member of the Electorial Board (“elektorsforsamlingen”)¨ of the Faculty of Science and Technology, UU, 2014- • Board of National Microscopy Infrastructure Comment: A Swedish infrastructure for advanced microscopy. Board of Upptech, 20160906 Comment: Upptech (Uppsala University School of Technology) has been established by the Faculty of Sci- ence and Technology at Uppsala University for raising visibility of, profiling, and enhancing the university’s research and teaching within technology. • Scientific Advisory Board of BioVis, 20160101 Comment: BioVis is a part of Uppsala University and associated with SciLifeLab, it is a core facility belonging to the Faculty of Medicine. • Board of SciLifeLab Uppsala, 20160401 Comment: Science for Life Laboratory, or SciLifeLab, is a national center for molecular biosciences with focus on health and environmental research. • Chairman of the Electorial Board (”elektorsforsamlingen”¨ of the Faculty of Science and Technology, UU, 2019- • Vice chairman and member of the review panel NT-19 (Biomedical Engineering) at the Swedish Research council • Member of the review committee of Halsa,¨ Medicin och Teknik - HMT 2020 • Steering board member - International Master program in Image Analysis and Machine Learning, 20190101– 20190630 • Member of the board of AI4Research, 20191218 Comment: AI4Research is a new initiative from Uppsala University to enhance interdisciplinary research involving artificial intelligence. • PhD halftime committee, 20191217 Comment: Halftime seminar by Fabio Socciarelli, supervised by Janne Lehtio.¨ Karolinska Institute, Solna.

101 Teo Asplund, Fredrik Nysjo,¨ Elisabeth Wetzer • Editors, SSBAktuellt, the newsletter of the Swedish Society for Automated Image Analysis

102