Bioimage Data Analysis Workflows.

Bioimage Data Analysis Workflows.

Saved in:
Bibliographic Details
Superior document:Learning Materials in Biosciences Series
:
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
Online Access:
Physical Description:1 online resource (178 pages)
Tags: Add Tag
No Tags, Be the first to tag this record!
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.

Similar Items