DOCSLIB.ORG

Creating an Open Source Infrastructure for Image Phenotyping in Clinical Research

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Creating an Open Source Infrastructure for Image Phenotyping in Clinical Research SIIM 2017 Scientific Session Posters & Demonstrations Creating an Open Source Infrastructure for Image Phenotyping in Clinical Research Brian E. Chapman, PhD, University of Utah; John A. Roberts, PhD; Adam Sorenson Background Biomedical images are a rich source of information that can be exploited for biological discovery. Mature ecosystems exist for phenotyping from micrscopy images (e.g. Carpenter 2006) but infrastructure for analysis of clinical images are less mature. While open source tools such as 3D Slicer (Pieper 2004) exist for image analysis, the image analysis software need to be incorporated into a larger eco system that provides case identification, subject de-identification with linkage to other data, knowledge based data management. In this presentation we describe an open-source eco system that can be used to facilitate phenotyping with biomedical images. Case Presentation We have built an image phenotyping eco system that is based entirely on open-source tools (primarily Orthanc, Girder, and Jupyter) and leverages Docker for ease of deployment and administration. Figure 1 shows the schematic of our system. Separate physical or logical networks are used to isolate services touching PHI. Further, Docker is used to provide individual compute environments for users interacting with data stored in our repository. Figure 1 We use existing research and operational databases to identify relevant cases of interest. Results of database queries are performed and stored within a HIPAA compliant infrastructure. Identified accession numbers, medical record numbers, and dates are then used to push identified cases from our clinical PACS using dcmtk (http://dicom.offis.de/dcmtk.php.en) scripts. Images are then pulled into an Orthanc system. Orthanc (http://www.orthanc-server.com/) is an open-source, lightweight DICOM server with a restful API that makes it highly configurable. We use a two-tier Orthanc system. The first (anonymizing) Orthanc server de-identifies the images using configurable Lua scripts, stores the identification information in a secure database, and then relays the anonymized images to a second (safe harbor) Orthanc server. We use the two-tier system to isolate PHI: downstream systems such as Girder never interact with the system containing PHI and the data flow is one-way within our Orthanc system: the safe harbor Orthanc server cannot pull data from the anonymizing server. Further scripts invoke the Orthanc restful API to push images from the safe harbor server into the Girder data store. We extend the capability of the anonymizing Orthanc server in order to support longitudinal studies. Images of the same subject acquired at different time points receive consistent anonymized patient identifiers. The Lua script maintains a map within the PostgreSQL database linking original and anonymized data. Further care is taken to maintain relative chronological order between anonymized longitudinal studies while still randomizing study dates as per HIPAA safe harbor requirements. Our data permanance is built using Girder (https://girder.readthedocs.io/en/latest/index.html) Girder is an open source, web-based data management system. Girder is agnostic to the type of data being stored but through a flexible plugin architecture can provide customized support for a variety of data types. Girder provides access management, and web visualization of data and associated meta data. Metadata are stored using JSON attribute-value pairs. In order to ensure effective data retrieval within Girder, all data have ontological metadata that describe the anatomical regions contained within the images as well as any disease(s) processes associated with the images (e.g. reason for exam or findings identified within the studies). We use bioportal (http://bioportal.bioontology.org/) as our ontology service. The persistent uniform resource locator (PURL) associated with the ontological terms are stored in the metadata for each image. We currently use the Uberon Ontology (http://uberon.github.io/) for anatomical concepts and the Human Disease Ontology (http://disease-ontology.org/) for disease concepts. When DICOM images are ingested into Girder, the public DICOM header fields are extracted into the JSON metadata structures associated with each data. DICOM images are transferred from Orthanc to Girder using web APIs for Orthanc and Girder. Data can be downloaded from Girder either through the web pages, through the restful API, or via direct interaction between the application and Girder. Applications such as 3D Slicer (https://www.slicer.org/) and XTK (https://github.com/xtk/X#readme) can be configured to directly interact with Girder, bypassing the need to download images and upload results. In addition to these specialized applications we have built a flexible interface to Girder based on Jupyter notebooks (http://jupyter.org/). Jupyter notebooks allow programmatic interaction with data through a web browser via a number of programming languages including Python and R. Data interactivity is achieved with JavaScript. Python is a particularly useful language for image analysis due to the large number of relevant packages available including SimpleITK, scikit-learn, and TensorFlow. Using nvidia-docker it is easy to provide GPU computing resources through the Jupyter notebook. We have create Python packages for intuitive interactions with Girder including Jupyter notebook widgets for data uploading that incorporates tagging data with ontological descriptions of anatomical regions and depicted disease (Figure 2). Figure 2 Outcome The image phenotyping infrastructure we have created is being used to support a number of clinical research projects including mammography, MR imaging of carotid disease, CTPA evaluation of pulmonary hypertension, and dermatological imaging of melanoma. Discussion The use of Docker greatly simplifies deploying and distributing the software. Jupyter notebooks allow for a powerful web-interface to the data which facilitates programming with Python which has a rich set of packages for medical image analysis. Serving the notebooks to users with Docker containers provides users isolated compute environments. Conclusion The software infrastructure we have created is built entirely using open source tools and will be freely distributed. Our infrastructure leverages existing projects and will allow us to leverage expanding web- based computing capabilities as they are developed. References 1. Carpenter, Anne E., et al. "CellProfiler: image analysis software for identifying and quantifying cell phenotypes." Genome biology 7.10 (2006): R100. 2. Pieper, Steve, Michael Halle, and Ron Kikinis. "3D Slicer." Biomedical Imaging: Nano to Macro, 2004. IEEE International Symposium on. IEEE, 2004. Keywords phenoyping, Python, Docker, Girder, Orthanc .
Load more

Recommended publications
  • Management of Large Sets of Image Data Capture, Databases, Image Processing, Storage, Visualization Karol Kozak
    Management of large sets of image data Capture, Databases, Image Processing, Storage, Visualization Karol Kozak Download free books at Karol Kozak Management of large sets of image data Capture, Databases, Image Processing, Storage, Visualization Download free eBooks at bookboon.com 2 Management of large sets of image data: Capture, Databases, Image Processing, Storage, Visualization 1st edition © 2014 Karol Kozak & bookboon.com ISBN 978-87-403-0726-9 Download free eBooks at bookboon.com 3 Management of large sets of image data Contents Contents 1 Digital image 6 2 History of digital imaging 10 3 Amount of produced images – is it danger? 18 4 Digital image and privacy 20 5 Digital cameras 27 5.1 Methods of image capture 31 6 Image formats 33 7 Image Metadata – data about data 39 8 Interactive visualization (IV) 44 9 Basic of image processing 49 Download free eBooks at bookboon.com 4 Click on the ad to read more Management of large sets of image data Contents 10 Image Processing software 62 11 Image management and image databases 79 12 Operating system (os) and images 97 13 Graphics processing unit (GPU) 100 14 Storage and archive 101 15 Images in different disciplines 109 15.1 Microscopy 109 360° 15.2 Medical imaging 114 15.3 Astronomical images 117 15.4 Industrial imaging 360° 118 thinking. 16 Selection of best digital images 120 References: thinking. 124 360° thinking . 360° thinking. Discover the truth at www.deloitte.ca/careers Discover the truth at www.deloitte.ca/careers © Deloitte & Touche LLP and affiliated entities. Discover the truth at www.deloitte.ca/careers © Deloitte & Touche LLP and affiliated entities.
    [Show full text]
  • Bioimage Analysis Tools
    Bioimage Analysis Tools Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, Ulrike Schulze, Simon Norrelykke, Christian Tischer, Thomas Pengo To cite this version: Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, et al.. Bioimage Analysis Tools. Kota Miura. Bioimage Data Analysis, Wiley-VCH, 2016, 978-3-527-80092-6. hal- 02910986 HAL Id: hal-02910986 https://hal.archives-ouvertes.fr/hal-02910986 Submitted on 3 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 2 Bioimage Analysis Tools 1 2 3 4 5 6 Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Pau/-Gilloteaux, - Ulrike Schulze,7 Simon F. Nerrelykke,8 Christian Tischer,9 and Thomas Penqo'" 1 European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany National Institute of Basic Biology, Okazaki, 444-8585, Japan 2/nstitute for Research in Biomedicine ORB Barcelona), Advanced Digital Microscopy, Parc Científic de Barcelona, dBaldiri Reixac 1 O, 08028 Barcelona, Spain 3German Center of Neurodegenerative
    [Show full text]
  • A 3D Interactive Multi-Object Segmentation Tool Using Local Robust Statistics Driven Active Contours
    A 3D interactive multi-object segmentation tool using local robust statistics driven active contours The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Gao, Yi, Ron Kikinis, Sylvain Bouix, Martha Shenton, and Allen Tannenbaum. 2012. A 3D Interactive Multi-Object Segmentation Tool Using Local Robust Statistics Driven Active Contours. Medical Image Analysis 16, no. 6: 1216–1227. doi:10.1016/j.media.2012.06.002. Published Version doi:10.1016/j.media.2012.06.002 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:28548930 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA NIH Public Access Author Manuscript Med Image Anal. Author manuscript; available in PMC 2013 August 01. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Med Image Anal. 2012 August ; 16(6): 1216–1227. doi:10.1016/j.media.2012.06.002. A 3D Interactive Multi-object Segmentation Tool using Local Robust Statistics Driven Active Contours Yi Gaoa,*, Ron Kikinisb, Sylvain Bouixa, Martha Shentona, and Allen Tannenbaumc aPsychiatry Neuroimaging Laboratory, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115 bSurgical Planning Laboratory, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115 cDepartments of Electrical and Computer Engineering and Biomedical Engineering, Boston University, Boston, MA 02115 Abstract Extracting anatomical and functional significant structures renders one of the important tasks for both the theoretical study of the medical image analysis, and the clinical and practical community.
    [Show full text]
  • Downloaded from the Cellprofiler Site [31] to Provide a Starting Point for New Analyses
    Open Access Software2006CarpenteretVolume al. 7, Issue 10, Article R100 CellProfiler: image analysis software for identifying and quantifying comment cell phenotypes Anne E Carpenter*, Thouis R Jones*†, Michael R Lamprecht*, Colin Clarke*†, In Han Kang†, Ola Friman‡, David A Guertin*, Joo Han Chang*, Robert A Lindquist*, Jason Moffat*, Polina Golland† and David M Sabatini*§ reviews Addresses: *Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA. †Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA. ‡Department of Radiology, Brigham and Women's Hospital, Boston, MA 02115, USA. §Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA. Correspondence: David M Sabatini. Email: [email protected] Published: 31 October 2006 Received: 15 September 2006 Accepted: 31 October 2006 reports Genome Biology 2006, 7:R100 (doi:10.1186/gb-2006-7-10-r100) The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2006/7/10/R100 © 2006 Carpenter et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. deposited research Cell<p>CellProfiler, image analysis the software first free, open-source system for flexible and high-throughput cell image analysis is described.</p> Abstract Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, research refereed CellProfiler.
    [Show full text]
  • Cellprofiler: Image Analysis Software for Identifying and Quantifying Cell Phenotypes
    CellProfiler: image analysis software for identifying and quantifying cell phenotypes The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Genome Biology. 2006 Oct 31;7(10):R100 As Published http://dx.doi.org/10.1186/gb-2006-7-10-r100 Publisher BioMed Central Ltd Version Final published version Citable link http://hdl.handle.net/1721.1/58762 Terms of Use Creative Commons Attribution Detailed Terms http://creativecommons.org/licenses/by/2.0 1 2 3 Table of Contents Getting Started: MaskImage . 96 Introduction . 6 Morph . 110 Installation . 7 OverlayOutlines . 111 Getting Started with CellProfiler . 9 PlaceAdjacent . 112 RescaleIntensity . 115 Resize . 116 Help: Rotate . 118 BatchProcessing . 11 Smooth . 122 Colormaps. .16 Subtract . 125 DefaultImageFolder . 17 SubtractBackground . 126 DefaultOutputFolder . 18 Tile.............................................127 DeveloperInfo . 19 FastMode . 28 MatlabCrash . 29 Object Processing modules: OutputFilename . 30 ClassifyObjects . 41 PixelSize . 31 ClassifyObjectsByTwoMeasurements . 42 Preferences. .32 ConvertToImage . 45 SkipErrors . 33 Exclude . 65 TechDiagnosis . 34 ExpandOrShrink . 66 FilterByObjectMeasurement . 71 IdentifyObjectsInGrid . 76 File Processing modules: IdentifyPrimAutomatic . 77 CreateBatchFiles . 51 IdentifyPrimManual . 83 ExportToDatabase . 68 IdentifySecondary . 84 ExportToExcel. .70 IdentifyTertiarySubregion. .88 LoadImages . 90 Relate . 113 LoadSingleImage . 94 LoadText. .95 RenameOrRenumberFiles .
    [Show full text]
  • An Open-Source Research Platform for Image-Guided Therapy
    Int J CARS DOI 10.1007/s11548-015-1292-0 ORIGINAL ARTICLE CustusX: an open-source research platform for image-guided therapy Christian Askeland1,3 · Ole Vegard Solberg1 · Janne Beate Lervik Bakeng1 · Ingerid Reinertsen1 · Geir Arne Tangen1 · Erlend Fagertun Hofstad1 · Daniel Høyer Iversen1,2,3 · Cecilie Våpenstad1,2 · Tormod Selbekk1,3 · Thomas Langø1,3 · Toril A. Nagelhus Hernes2,3 · Håkon Olav Leira2,3 · Geirmund Unsgård2,3 · Frank Lindseth1,2,3 Received: 3 July 2015 / Accepted: 31 August 2015 © The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Results The validation experiments show a navigation sys- Purpose CustusX is an image-guided therapy (IGT) research tem accuracy of <1.1mm, a frame rate of 20 fps, and latency platform dedicated to intraoperative navigation and ultra- of 285ms for a typical setup. The current platform is exten- sound imaging. In this paper, we present CustusX as a robust, sible, user-friendly and has a streamlined architecture and accurate, and extensible platform with full access to data and quality process. CustusX has successfully been used for algorithms and show examples of application in technologi- IGT research in neurosurgery, laparoscopic surgery, vascular cal and clinical IGT research. surgery, and bronchoscopy. Methods CustusX has been developed continuously for Conclusions CustusX is now a mature research platform more than 15years based on requirements from clinical for intraoperative navigation and ultrasound imaging and is and technological researchers within the framework of a ready for use by the IGT research community. CustusX is well-defined software quality process. The platform was open-source and freely available at http://www.custusx.org.
    [Show full text]
  • Medical Image Processing Software
    Wohlers Report 2018 Medical Image Processing Software Medical image Patient-specific medical devices and anatomical models are almost always produced using radiological imaging data. Medical image processing processing software is used to translate between radiology file formats and various software AM file formats. Theoretically, any volumetric radiological imaging dataset by Andy Christensen could be used to create these devices and models. However, without high- and Nicole Wake quality medical image data, the output from AM can be less than ideal. In this field, the old adage of “garbage in, garbage out” definitely applies. Due to the relative ease of image post-processing, computed tomography (CT) is the usual method for imaging bone structures and contrast- enhanced vasculature. In the dental field and for oral- and maxillofacial surgery, in-office cone-beam computed tomography (CBCT) has become popular. Another popular imaging technique that can be used to create anatomical models is magnetic resonance imaging (MRI). MRI is less useful for bone imaging, but its excellent soft tissue contrast makes it useful for soft tissue structures, solid organs, and cancerous lesions. Computed tomography: CT uses many X-ray projections through a subject to computationally reconstruct a cross-sectional image. As with traditional 2D X-ray imaging, a narrow X-ray beam is directed to pass through the subject and project onto an opposing detector. To create a cross-sectional image, the X-ray source and detector rotate around a stationary subject and acquire images at a number of angles. An image of the cross-section is then computed from these projections in a post-processing step.
    [Show full text]
  • Titel Untertitel
    KNIME Image Processing Nycomed Chair for Bioinformatics and Information Mining Department of Computer and Information Science Konstanz University, Germany Why Image Processing with KNIME? KNIME UGM 2013 2 The “Zoo” of Image Processing Tools Development Processing UI Handling ImgLib2 ImageJ OMERO OpenCV ImageJ2 BioFormats MatLab Fiji … NumPy CellProfiler VTK Ilastik VIGRA CellCognition … Icy Photoshop … = Single, individual, case specific, incompatible solutions KNIME UGM 2013 3 The “Zoo” of Image Processing Tools Development Processing UI Handling ImgLib2 ImageJ OMERO OpenCV ImageJ2 BioFormats MatLab Fiji … NumPy CellProfiler VTK Ilastik VIGRA CellCognition … Icy Photoshop … → Integration! KNIME UGM 2013 4 KNIME as integration platform KNIME UGM 2013 5 Integration: What and How? KNIME UGM 2013 6 Integration ImgLib2 • Developed at MPI-CBG Dresden • Generic framework for data (image) processing algoritms and data-structures • Generic design of algorithms for n-dimensional images and labelings • http://fiji.sc/wiki/index.php/ImgLib2 → KNIME: used as image representation (within the data cells); basis for algorithms KNIME UGM 2013 7 Integration ImageJ/Fiji • Popular, highly interactive image processing tool • Huge base of available plugins • Fiji: Extension of ImageJ1 with plugin-update mechanism and plugins • http://rsb.info.nih.gov/ij/ & http://fiji.sc/ → KNIME: ImageJ Macro Node KNIME UGM 2013 8 Integration ImageJ2 • Next-generation version of ImageJ • Complete re-design of ImageJ while maintaining backwards compatibility • Based on ImgLib2
    [Show full text]
  • 9. Biomedical Imaging Informatics Daniel L
    9. Biomedical Imaging Informatics Daniel L. Rubin, Hayit Greenspan, and James F. Brinkley After reading this chapter, you should know the answers to these questions: 1. What makes images a challenging type of data to be processed by computers, as opposed to non-image clinical data? 2. Why are there many different imaging modalities, and by what major two characteristics do they differ? 3. How are visual and knowledge content in images represented computationally? How are these techniques similar to representation of non-image biomedical data? 4. What sort of applications can be developed to make use of the semantic image content made accessible using the Annotation and Image Markup model? 5. Describe four different types of image processing methods. Why are such methods assembled into a pipeline when creating imaging applications? 6. Give an example of an imaging modality with high spatial resolution. Give an example of a modality that provides functional information. Why are most imaging modalities not capable of providing both? 7. What is the goal in performing segmentation in image analysis? Why is there more than one segmentation method? 8. What are two types of quantitative information in images? What are two types of semantic information in images? How might this information be used in medical applications? 9. What is the difference between image registration and image fusion? Given an example of each. 1 9.1. Introduction Imaging plays a central role in the healthcare process. Imaging is crucial not only to health care, but also to medical communication and education, as well as in research.
    [Show full text]
  • An Open Source Freeware Software for Ultrasound Imaging and Elastography
    Proceedings of the eNTERFACE’07 Workshop on Multimodal Interfaces, Istanbul,˙ Turkey, July 16 - August 10, 2007 USIMAGTOOL: AN OPEN SOURCE FREEWARE SOFTWARE FOR ULTRASOUND IMAGING AND ELASTOGRAPHY Ruben´ Cardenes-Almeida´ 1, Antonio Tristan-Vega´ 1, Gonzalo Vegas-Sanchez-Ferrero´ 1, Santiago Aja-Fernandez´ 1, Veronica´ Garc´ıa-Perez´ 1, Emma Munoz-Moreno˜ 1, Rodrigo de Luis-Garc´ıa 1, Javier Gonzalez-Fern´ andez´ 2, Dar´ıo Sosa-Cabrera 2, Karl Krissian 2, Suzanne Kieffer 3 1 LPI, University of Valladolid, Spain 2 CTM, University of Las Palmas de Gran Canaria 3 TELE Laboratory, Universite´ catholique de Louvain, Louvain-la-Neuve, Belgium ABSTRACT • Open source code: to be able for everyone to modify and reuse the source code. UsimagTool will prepare specific software for the physician to change parameters for filtering and visualization in Ultrasound • Efficiency, robust and fast: using a standard object ori- Medical Imaging in general and in Elastography in particular, ented language such as C++. being the first software tool for researchers and physicians to • Modularity and flexibility for developers: in order to chan- compute elastography with integrated algorithms and modular ge or add functionalities as fast as possible. coding capabilities. It will be ready to implement in different • Multi-platform: able to run in many Operating systems ecographic systems. UsimagTool is based on C++, and VTK/ITK to be useful for more people. functions through a hidden layer, which means that participants may import their own functions and/or use the VTK/ITK func- • Usability: provided with an easy to use GUI to interact tions. as easy as possible with the end user.
    [Show full text]
  • Development and Characterization of the Invesalius Navigator Software for Navigated Transcranial Magnetic Stimulation T ⁎ Victor Hugo Souzaa, ,1, Renan H
    Journal of Neuroscience Methods 309 (2018) 109–120 Contents lists available at ScienceDirect Journal of Neuroscience Methods journal homepage: www.elsevier.com/locate/jneumeth Development and characterization of the InVesalius Navigator software for navigated transcranial magnetic stimulation T ⁎ Victor Hugo Souzaa, ,1, Renan H. Matsudaa, André S.C. Peresa,b, Paulo Henrique J. Amorimc, Thiago F. Moraesc, Jorge Vicente L. Silvac, Oswaldo Baffaa a Departamento de Física, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes, 3900, 14040-901, Ribeirão Preto, SP, Brazil b Instituto Internacional de Neurociência de Natal Edmond e Lily Safra, Instituto Santos Dumont, Rodovia RN 160 Km 03, 3003, 59280-000, Macaíba, RN, Brazil c Núcleo de Tecnologias Tridimensionais, Centro de Tecnologia da Informação Renato Archer, Rodovia Dom Pedro I Km 143, 13069-901, Campinas, SP, Brazil GRAPHICAL ABSTRACT ARTICLE INFO ABSTRACT Keywords: Background: Neuronavigation provides visual guidance of an instrument during procedures of neurological in- Neuronavigation terventions, and has been shown to be a valuable tool for accurately positioning transcranial magnetic stimu- Transcranial magnetic stimulation lation (TMS) coils relative to an individual’s anatomy. Despite the importance of neuronavigation, its high cost, Localization error low portability, and low availability of magnetic resonance imaging facilities limit its insertion in research and Co-registration clinical environments. Coil positioning New method: We have developed and validated the InVesalius Navigator as the first free, open-source software Surgical planning for image-guided navigated TMS, compatible with multiple tracking devices. A point-based, co-registration al- gorithm and a guiding interface were designed for tracking any instrument (e.g.
    [Show full text]
  • Kitware Source Issue 21
    SOFTWARE DEVELOPER’S QUARTERLY Issue 21 • April 2012 Editor’s Note ........................................................................... 1 PARAVIEW 3.14 RELEASED In late February, Kitware and the ParaView team released Recent Releases ..................................................................... 1 ParaView 3.14. This release features usability enhancements, improvements to the Plugin framework, new panels, and ParaView in Immersive Environments .................................. 3 more than 100 other resolved issues. Teaching Open Source .......................................................... 4 ParaView 3.14 features a redesigned Find Data dialog and Color Editor. The updated Find Data dialog makes it possible ParaView Query Selection Framework................................. 7 to use complex queries to select elements, including combin- ing multiple test cases with Boolean operations. The Color Ginkgo CADx: Open Source DICOM CADx Environment .... 8 Editor, used to edit lookup tables or color tables for scalar mapping, now enables independent editing of the color and Code Review, Topic Branches and VTK ................................. 9 opacity functions. Video Analysis on Business Intelligence, ParaView’s charting capabilities have been extended with a Studies in Computational Intelligence ............................... 11 new scatter plot matrix view and the ability to visualize mul- tiple dimensions of data in one compact form. This improved The Visible Patient ..............................................................
    [Show full text]