Bioimage Informatics in the Context of Drosophila Research
Total Page:16
File Type:pdf, Size:1020Kb
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. -
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 -
Other Departments and Institutes Courses 1
Other Departments and Institutes Courses 1 Other Departments and Institutes Courses About Course Numbers: 02-250 Introduction to Computational Biology Each Carnegie Mellon course number begins with a two-digit prefix that Spring: 12 units designates the department offering the course (i.e., 76-xxx courses are This class provides a general introduction to computational tools for biology. offered by the Department of English). Although each department maintains The course is divided into two halves. The first half covers computational its own course numbering practices, typically, the first digit after the prefix molecular biology and genomics. It examines important sources of biological indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses data, how they are archived and made available to researchers, and what are sophomore level, etc. Depending on the department, xx-6xx courses computational tools are available to use them effectively in research. may be either undergraduate senior-level or graduate-level, and xx-7xx In the process, it covers basic concepts in statistics, mathematics, and courses and higher are graduate-level. Consult the Schedule of Classes computer science needed to effectively use these resources and understand (https://enr-apps.as.cmu.edu/open/SOC/SOCServlet/) each semester for their results. Specific topics covered include sequence data, searching course offerings and for any necessary pre-requisites or co-requisites. and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. The second half covers computational cell biology, including biological modeling and image analysis. It includes homework requiring Computational Biology Courses modification of scripts to perform computational analyses. -
Bio-Formats Documentation Release 4.4.9
Bio-Formats Documentation Release 4.4.9 The Open Microscopy Environment October 15, 2013 CONTENTS I About Bio-Formats 2 1 Why Java? 4 2 Bio-Formats metadata processing 5 3 Help 6 3.1 Reporting a bug ................................................... 6 3.2 Troubleshooting ................................................... 7 4 Bio-Formats versions 9 4.1 Version history .................................................... 9 II User Information 23 5 Using Bio-Formats with ImageJ and Fiji 24 5.1 ImageJ ........................................................ 24 5.2 Fiji .......................................................... 25 5.3 Bio-Formats features in ImageJ and Fiji ....................................... 26 5.4 Installing Bio-Formats in ImageJ .......................................... 26 5.5 Using Bio-Formats to load images into ImageJ ................................... 28 5.6 Managing memory in ImageJ/Fiji using Bio-Formats ................................ 32 5.7 Upgrading the Bio-Formats importer for ImageJ to the latest trunk build ...................... 34 6 OMERO 39 7 Image server applications 40 7.1 BISQUE ....................................................... 40 7.2 OME Server ..................................................... 40 8 Libraries and scripting applications 43 8.1 Command line tools ................................................. 43 8.2 FARSIGHT ...................................................... 44 8.3 i3dcore ........................................................ 44 8.4 ImgLib ....................................................... -
Bio-Formats Documentation Release 5.2.2
Bio-Formats Documentation Release 5.2.2 The Open Microscopy Environment September 12, 2016 CONTENTS I About Bio-Formats 2 1 Help 4 2 Bio-Formats versions 5 3 Why Java? 6 4 Bio-Formats metadata processing 7 4.1 Reporting a bug ................................................... 7 4.2 Version history .................................................... 8 II User Information 38 5 Using Bio-Formats with ImageJ and Fiji 39 5.1 ImageJ overview ................................................... 39 5.2 Fiji overview ..................................................... 41 5.3 Bio-Formats features in ImageJ and Fiji ....................................... 42 5.4 Installing Bio-Formats in ImageJ .......................................... 42 5.5 Using Bio-Formats to load images into ImageJ ................................... 44 5.6 Managing memory in ImageJ/Fiji using Bio-Formats ................................ 48 6 Command line tools 51 6.1 Command line tools introduction .......................................... 51 6.2 Displaying images and metadata ........................................... 52 6.3 Converting a file to different format ......................................... 54 6.4 Validating XML in an OME-TIFF .......................................... 56 6.5 Editing XML in an OME-TIFF ........................................... 57 6.6 List formats by domain ................................................ 58 6.7 List supported file formats .............................................. 58 6.8 Display file in ImageJ ............................................... -
Bioimage Informatics
Bioimage Informatics Lecture 22, Spring 2012 Empirical Performance Evaluation of Bioimage Informatics Algorithms Image Database; Content Based Image Retrieval Emerging Applications: Molecular Imaging Lecture 22 April 23, 2012 1 Outline • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 2 • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 3 Overview of Algorithm Evaluation (I) • Two sets of data are required - Input data - Corresponding correct output (ground truth) • Sources of input data - Actual/experimental data, often from experiments - Synthetic data, often from computer simulation • Actual/experimental data - Essential for performance evaluation - May be costly to acquire - May not be representative - Ground truth often is unknown Overview of Algorithm Evaluation (II) • What exactly is ground truth? Ground truth is a term used in cartography, meteorology, analysis of aerial photographs, satellite imagery and a range of other remote sensing techniques in which data are gathered at a distance. Ground truth refers to information that is collected "on location." - http://en.wikipedia.org/wiki/Ground_truth • Synthetic (simulated) data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data 5 Overview of Algorithm Evaluation (III) • Simulated data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data • Realistic synthetic data - To use as much information from real experimental data as possible • Quality control of manual analysis Test Protocol Development (I) • Implementation - Source codes are not always available and often are on different platforms. • Parameter setting - This is one of the most challenging issues. -
Imagej User Guide Contents Release Notes for Imagej 1.46R Vii IJ 1.46R Noteworthy Viii
RevisedIJ 1.46r edition ImageJ UserUser Guide Guide ImageJImageJ/Fiji 1.46 ImageJ User Guide Contents Release Notes for ImageJ 1.46r vii IJ 1.46r Noteworthy viii Tiago Ferreira • Wayne Rasband IJ User Guide | Booklet 1 Macro Listings ix Conventions x I Getting Started 1 Introduction 1 2 Installing and Maintaining ImageJ2 2.1 ImageJDistributions.................................. 2 2.2 Related Software ................................... 3 2.3 ImageJ2 ........................................ 5 3 Getting Help5 3.1 Help on Image Analysis................................ 5 3.2 Help on ImageJ.................................... 6 II Working with ImageJ 4 Using Keyboard Shortcuts8 5 Finding Commands8 6 Undo and Redo9 7 Image Types and Formats 10 Tuesday 2nd October, 2012 8 Stacks, Virtual Stacks and Hyperstacks 12 9 Color Images 14 10 Selections 17 Foreword 10.1 Manipulating ROIs .................................. 18 10.2 Composite Selections................................. 19 The ImageJ User Guide provides a detailed overview of ImageJ (and inherently Fiji), 10.3 Selections With Sub-pixel Coordinates ....................... 19 the standard in scientific image analysis (see XXVI Focus on Bioimage Informatics). It was thought as a comprehensive, fully-searchable, self-contained, annotatable 11 Overlays 19 manual (see Conventions Used in this Guide). A HTML version is also available as 12 3D Volumes 21 well as printer-friendly booklets (see Guide Formats). Its latest version can always be obtained from http://imagej.nih.gov/ij/docs/guide. The source files are available 13 Settings and Preferences 22 through a Git version control repository at http://fiji.sc/guide.git. Given ImageJ’s heavy development this guide will always remain incomplete. All Im- ageJ users and developers are encouraged to contribute to the ImageJ documentation III Extending ImageJ resources (see Getting Involved). -
Bioimage Informatics 2019
BioImage Informatics 2019 Co-hosted by: October 2-4, 2019 Allen Institute 615 Westlake Avenue N Seattle, WA 98109 alleninstitute.org/bioimage-informatics twitter #bioimage19 THANKS TO OUR SPONSORS BioImage Informatics 2019 Wednesday, October 2 8:00-9:00am Registration and continental breakfast 9:00-9:15am Welcome and introductory remarks, BioImage Informatics Local Planning Committee • Michael Hawrylycz, Ph.D., Allen Institute for Brain Science (co-chair) • Winfried Wiegraebe, Ph.D., Allen Institute for Cell Science (co-chair) • Forrest Collman, Ph.D., Allen Institute for Brain Science • Greg Johnson, Ph.D., Allen Institute for Cell Science • Lydia Ng, Ph.D., Allen Institute for Brain Science • Kaitlyn Casimo, Ph.D., Allen Institute 9:15-10:15am Opening keynote Gaudenz Danuser, UT Southwestern Medical Center “Inference of causality in molecular pathways from live cell images” Defining cause-effect relations among molecular components is the holy grail of every cell biological study. It turns out that for many of the molecular pathways this task is far from trivial. Many of the pathways are characterized by functional overlap and nonlinear interactions between components. In this configuration, perturbation of one component may result in a measureable shift of the pathway output – commonly referred to as a phenotype – but it is strictly impossible to interpret the phenotype in terms of the roles the targeted component plays in the unperturbed system. This caveat of perturbation approaches applies equally to genetic perturbations, which lead to long-term adaptation, and more acute pharmacological and optogenetic approaches, which often induce short-term adaptation. For nearly two decades my lab has been devoted to circumventing this problem by exploiting basal fluctuations of unperturbed systems to establish cause-and-effect cascades between pathway components. -
A Bioimage Informatics Platform for High-Throughput Embryo Phenotyping James M
Briefings in Bioinformatics, 19(1), 2018, 41–51 doi: 10.1093/bib/bbw101 Advance Access Publication Date: 14 October 2016 Paper A bioimage informatics platform for high-throughput embryo phenotyping James M. Brown, Neil R. Horner, Thomas N. Lawson, Tanja Fiegel, Simon Greenaway, Hugh Morgan, Natalie Ring, Luis Santos, Duncan Sneddon, Lydia Teboul, Jennifer Vibert, Gagarine Yaikhom, Henrik Westerberg and Ann-Marie Mallon Corresponding author: James Brown, MRC Harwell Institute, Harwell Campus, Oxfordshire, OX11 0RD. Tel. þ44-0-1235-841237; Fax: +44-0-1235-841172; E-mail: [email protected] Abstract High-throughput phenotyping is a cornerstone of numerous functional genomics projects. In recent years, imaging screens have become increasingly important in understanding gene–phenotype relationships in studies of cells, tissues and whole organisms. Three-dimensional (3D) imaging has risen to prominence in the field of developmental biology for its ability to capture whole embryo morphology and gene expression, as exemplified by the International Mouse Phenotyping Consortium (IMPC). Large volumes of image data are being acquired by multiple institutions around the world that encom- pass a range of modalities, proprietary software and metadata. To facilitate robust downstream analysis, images and metadata must be standardized to account for these differences. As an open scientific enterprise, making the data readily accessible is essential so that members of biomedical and clinical research communities can study the images for them- selves without the need for highly specialized software or technical expertise. In this article, we present a platform of soft- ware tools that facilitate the upload, analysis and dissemination of 3D images for the IMPC. -
View of Above Observations and Arguments, the Fol- Scopy Environment) [9]
Loyek et al. BMC Bioinformatics 2011, 12:297 http://www.biomedcentral.com/1471-2105/12/297 SOFTWARE Open Access BioIMAX: A Web 2.0 approach for easy exploratory and collaborative access to multivariate bioimage data Christian Loyek1*, Nasir M Rajpoot2, Michael Khan3 and Tim W Nattkemper1 Abstract Background: Innovations in biological and biomedical imaging produce complex high-content and multivariate image data. For decision-making and generation of hypotheses, scientists need novel information technology tools that enable them to visually explore and analyze the data and to discuss and communicate results or findings with collaborating experts from various places. Results: In this paper, we present a novel Web2.0 approach, BioIMAX, for the collaborative exploration and analysis of multivariate image data by combining the webs collaboration and distribution architecture with the interface interactivity and computation power of desktop applications, recently called rich internet application. Conclusions: BioIMAX allows scientists to discuss and share data or results with collaborating experts and to visualize, annotate, and explore multivariate image data within one web-based platform from any location via a standard web browser requiring only a username and a password. BioIMAX can be accessed at http://ani.cebitec. uni-bielefeld.de/BioIMAX with the username “test” and the password “test1” for testing purposes. Background key challenge of bioimage informatics is to present the In the last two decades, innovative biological and biome- large amount of image data in a way that enables them dical imaging technologies greatly improved our under- to be browsed, analyzed, queried or compared with standing of structures of molecules, cells and tissue and other resources [2]. -
Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns
Principles of Bioimage Informatics: Focus on Machine Learning of Cell Patterns Luis Pedro Coelho1,2,3, Estelle Glory-Afshar3,6, Joshua Kangas1,2,3, Shannon Quinn2,3,4, Aabid Shariff1,2,3, and Robert F. Murphy1,2,3,4,5,6 1 Joint Carnegie Mellon University–University of Pittsburgh Ph.D. Program in Computational Biology 2 Lane Center for Computational Biology, Carnegie Mellon University 3 Center for Bioimage Informatics, Carnegie Mellon University 4 Department of Biological Sciences, Carnegie Mellon University 5 Machine Learning Department, Carnegie Mellon University 6 Department of Biomedical Engineering, Carnegie Mellon University Abstract. The field of bioimage informatics concerns the development and use of methods for computational analysis of biological images. Tra- ditionally, analysis of such images has been done manually. Manual an- notation is, however, slow, expensive, and often highly variable from one expert to another. Furthermore, with modern automated microscopes, hundreds to thousands of images can be collected per hour, making man- ual analysis infeasible. This field borrows from the pattern recognition and computer vi- sion literature (which contain many techniques for image processing and recognition), but has its own unique challenges and tradeoffs. Fluorescence microscopy images represent perhaps the largest class of biological images for which automation is needed. For this modality, typical problems include cell segmentation, classification of phenotypical response, or decisions regarding differentiated responses (treatment vs. control setting). This overview focuses on the problem of subcellular lo- cation determination as a running example, but the techniques discussed are often applicable to other problems. 1 Introduction Bioimage informatics employs computational and statistical techniques to ana- lyze images and related metadata. -
Introduction to Bio(Medical)- Informatics
Introduction to Bio(medical)- Informatics Kun Huang, PhD Raghu Machiraju, PhD Department of Biomedical Informatics Department of Computer Science and Engineering The Ohio State University Outline • Overview of bioinformatics • Other people’s (public) data • Basic biology review • Basic bioinformatics techniques • Logistics 2 Technical areas of bioinformatics IMMPORT https://immport.niaid.nih.gov/immportWeb/home/home.do?loginType=full# Databases and Query Databases and Query 547 5 Visualization 6 Visualization http://supramap.osu.edu 7 Network 8 Visualization and Visual Analytics § Visualizing genome data annotation 9 Network Mining Hopkins 2007, Nature Biotechnology, Network pharmacology 10 Role of Bioinformatics – Hypothesis Generation Hypothesis generation Bioinformatics Hypothesis testing 11 Training for Bioinformatics § Biology / medicine § Computing – programming, algorithm, pattern recognition, machine learning, data mining, network analysis, visualization, software engineering, database, etc § Statistics – hypothesis testing, permutation method, multivariate analysis, Bayesian method, etc Beyond Bioinformatics • Translational Bioinformatics • Biomedical Informatics • Medical Informatics • Nursing Informatics • Imaging Informatics • Bioimage Informatics • Clinical Research Informatics • Pathology Informatics • … • Health Analytics • … • Legal Informatics • Business Informatics Source: Department of Biomedical Informatics 13 Beyond Bioinformatics Basic Science Clinical Research Informatics Biomedical Informatics Theories & Methods