Bioimage Data Analysis Workflows.

• Superior document: Learning Materials in Biosciences Series
• Author: Miura, Kota.
• TeilnehmendeR: Sladoje, Natasa.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2019.; ©2020.
• Year of Publication: 2019
• Edition: 1st ed.
• Language: English
• Series: Learning Materials in Biosciences Series
• Physical Description: 1 online resource (178 pages)

Table of Contents

Intro -- Preface -- Acknowledgements -- Contents -- Contributors -- 1: Workflows and Components of Bioimage Analysis -- 1.1 Introduction -- 1.2 Types of Bioimage Analysis Software -- Bibliography -- 2: Measurements of Intensity Dynamics at the Periphery of the Nucleus -- 2.1 Introduction -- 2.2 Tools -- 2.3 Dataset -- 2.4 Workflow -- 2.4.1 Segmentation of Nucleus Rim -- 2.4.1.1 Block 1: Splitting Channels -- 2.4.1.2 Block 2: Segmentation of Nucleus Rim -- 2.4.1.3 Block 3: Intensity Measurement Using Mask -- 2.4.2 Integration: The Measurement Over Time -- 2.4.3 Integrating Segmentation and Measurements -- 2.5 Results and Conclusion -- 2.6 Exercise Answers -- 2.6.1 Exercises 2.1-2.4 -- 2.6.2 Exercise 2.5 -- Bibliography -- 3: 3D Quantitative Colocalisation Analysis -- 3.1 Introduction -- 3.1.1 What Is Colocalisation? -- 3.1.2 Which Colocalisation Methods Are There? -- 3.1.3 Some Image Preprocessing Tips You Should Keep in Mind -- 3.2 Datasets -- 3.3 Tools -- 3.4 Workflow 1: Objects Overlap Volume Quantification -- 3.4.1 Step 0: Building a Strategy -- 3.4.2 Step 1: Normalize the Image Names -- 3.4.3 Step 2: Tag the Objects -- 3.4.4 Step 3: Isolating the Overlapping Parts -- 3.4.5 Step 4: Retrieve Volumes -- 3.4.6 Step 5: Generate Outputs -- 3.4.7 Step 6: Make the Macro User Friendly -- 3.4.8 What Then? -- 3.5 Workflow 2: Objects Overlap Intensity Quantification -- 3.5.1 What Should We Do? -- 3.5.2 New Step 4: Retrieve Intensities -- 3.5.3 Adapted Step 6: Make the Macro User Friendly -- Bibliography -- 4: The NEMO Dots Assembly: Single-Particle Tracking and Analysis -- 4.1 Introduction -- 4.2 Datasets -- 4.3 Tools and Prerequisites -- 4.4 Workflow -- 4.5 Single-Particle Tracking with TrackMate -- 4.5.1 Step 1: Loading Image Data and Launching TrackMate -- 4.5.2 Step 2: Detection -- 4.5.3 Step 3: Filtering.

4.5.4 Step 4: Particle-Linking -- 4.5.5 Step 5: Filtering Tracks -- 4.5.6 Step 6: Export Results -- 4.6 Motility Analysis with Mean-Square Displacement -- 4.6.1 Step 1: Importing Tracks into MATLAB -- 4.6.2 Step 2: Create and Add Data to the MSD Analyzer -- 4.6.3 Interlude: A Short Word About Mean-Square Displacement Analysis -- 4.6.4 Step 3: Compute the Mean-Square Displacement -- 4.6.5 Step 4: Log-Log Fit of the Mean-Square Displacement -- 4.6.6 Step 5: Analysis of the Log-Log Fit -- 4.7 Results and Conclusion -- Bibliography -- 5: Introduction to MATLAB -- 5.1 Tools -- 5.1.1 MATLAB -- 5.1.2 Image Processing Toolbox -- 5.1.3 Statistics and Machine Learning Toolbox, Curve Fitting Toolbox -- 5.2 Getting Started with MATLAB -- 5.2.1 Baby Steps -- 5.2.2 Plot Something -- 5.2.3 Make it Pretty -- 5.2.4 Getting Help -- 5.3 Automating It: Creating Your Own Programs -- 5.3.1 Create, Save, and Run Scripts -- 5.3.2 Code Folding and Block-Wise Execution -- 5.3.3 Scripts, Programs, Functions: Nomenclature -- 5.4 Working with Images -- 5.4.1 Reading and Displaying an Image -- 5.4.2 Extracting Meta-Data from an Image -- 5.4.3 Reading and Displaying an Image-Stack -- 5.4.4 Smoothing, Thresholding and All That -- 5.5 Time-Series Analysis -- 5.5.1 Simulating a Time-Series of Brownian Motion (Random Walk) -- 5.5.2 Plotting a Time-Series -- 5.5.3 Histograms -- 5.5.4 Sub-Sampling a Time-Series (Slicing and Accessing Data) -- 5.5.5 Investigating How "Speed" Depends on Δt -- 5.5.6 Investigating How "Speed" Depends on Subsampling -- 5.5.7 Simulating Confined Brownian Motion -- 5.5.8 Simulating Directed Motion with Random Tracking Error -- 5.5.9 Loading Tracking Data from a File -- 5.5.10 Smoothing (Filtering) a Time-Series -- 5.6 MSD: Mean Square Displacement -- 5.6.1 Creating a Function That Calculates MSDs.

5.6.1.1 About Functions and How to Call Them -- 5.6.2 MSD: Linear Motion -- 5.6.3 MSD: Brownian Motion -- 5.6.3.1 MSD: Simulated Random Walk -- 5.6.4 MSD: Averaged Over Several 2-Dim Tracks -- 5.6.5 Further Reading About Diffusion, the MSD, and Fitting Power-Laws -- Appendix: MATLAB Fundamental Data Classes -- MATLAB Documentation Keywords for Data Classes -- Appendix: Do I Have That Toolbox? -- Appendix: HTML and Live Scripts -- Publish Your Script to HTML -- Working with Live Scripts -- Appendix: Getting File and Folder Names Automatically -- Read from a Folder -- Path and File Names -- Appendix: Codehygiene -- Appendix: MATLAB Cheat Sheet -- Bibliography -- 6: Resolving the Process of Clathrin Mediated Endocytosis Using Correlative Light and Electron Microscopy (CLEM) -- 6.1 Introduction -- 6.2 Data Presentation -- 6.3 Overview of Data Processing -- 6.4 Tools Description -- 6.5 Application to a CLEM Experiment -- 6.5.1 CLEM Workflow Overview and Preparation -- 6.5.2 Labeling of Landmark Pairs -- 6.5.2.1 Correlation from Low Magnification Tomogram to High Magnification EM Image -- 6.5.3 Generating the Transformation -- 6.5.4 Applying the Transformation to Image and Coordinate Data -- 6.5.4.1 Transforming Images -- 6.5.4.2 Transforming Coordinates -- 6.5.5 Registering the Low-Magnification and the High-Magnification EM Data -- 6.6 Accuracy Estimation and Improvements -- Appendix: Image Transformations -- Basic Similarity and Affine Transformations -- Higher-Order Transformations -- Generating Transformations from Image Coordinates -- Bibliography -- Index.