Table of Contents

BIAS 2016: Syllabus

ImageJ basics (Day 1)

This module is made of two parts: the first part introduces the organization of the menus in Fiji, the basic objects that can be handled (images, stacks, selections) and basic image processing techniques (filtering, segmentation, intensity and geometrical measurements, mask analysis). The second part deals with the macro language and batch processing.

Basics of Handling, Processing and Analyzing Image Objects in ImageJ

Image File I/O and Image Formats

Using Region-Of-Interest (ROI)

Image manipulation

Image filtering

Image segmentation

Post-processing of Masks

Connected Regions and Particles Analysis

Writing ImageJ Macros for Efficient Processing and Analysis

Simple example macros (possible candidates):

Matlab basics (Day 3)

The matlab basics module introduces the tools we need for the applications on days 3 and 4. We get familiar with the matlab programming environment and learn the basics about variable types, calculations and matrix operations. In small applied projects, we import and visualize a time lapse image sequence by using a for loop and the imread function. We visualize the grey value histogram, normalize and filter images and detect image objects by thresholding. We extract image object features and visualize them by using the plot function.

Applications (Day 2, 3 & 4)

P4: FISH spots counting in human spermatozoids

Task

Batch processing with ImageJ macro, spot detection, results handling. Write an ImageJ macro to detect the spermatozoids nuclei and count the number of FISH spots inside each of them in three different channels.

Input data

Tiling of a part of the sample with motorized confocal microscope (Leica SP5): 9 fields of view (5 z planes, 4 channels: DAPI + 3 channels for different FISH signals).

Software

ImageJ Macro (1.0)

Workflow

  1. Detection of the candidate nuclei
    • Spatial filtering
    • MIP
    • Global/local thresholding
    • Particle analysis
  2. Detection of the candidate spots inside the nuclei
    • Laplacian filtering
    • MIP
    • Experiment different strategies for spot detection
      1. remove outliers
      2. FindPeaks plugin
  3. Results management
    • Overlay the detected spots over the MIP images
    • Overlay the detected nuclei with a color code according to the number of spots detected in each channel
    • Select the nuclei checking some given combinations of spot multiplicity per channel

Misc

Scheduled on: Day 2
Main Contributor: Sebastien

P5: Quantitative evaluation of muticellullar movement in Drosophila

Task

Introduce the concept of tracking. Describe the ImageJ “particle tracker” plugin and discuss its limitations for object tracking. Introduce a simple method for cell tracking. Plot of area distributions across time. Number of neighbors and vertex model.

Input data

Cadherin expressing drosophila embryo tissue. MIP of spinning disk movies.

Software

ImageJ Macro + Matlab (0.5 : 0.5)

Workflow

  1. ImageJ macro: detection of candidate cells
    • Laplacian filtering
    • Detection of regional maxima
    • Watershed from regional maxima
    • Optional step: suppress weak segments
      • segmentation mask (labels)
  2. Object tracking
    • Overlap based tracking
    • Refinements
  3. Vertices extraction
    • Analyze skeleton
    • Associate vertices to cells: compute and represent the velocity field of the vertices
  4. Optional: Comparison with a PIV analysis (Matlab)
  5. Optional: Introduce the vertex model

Misc

Scheduled on: Day 3
Main Contributor: Sebastien

P7: 3D visualization and quantification of Blood Vessels inside a Subcutaneous Tumour

Task

Filament segmentation and analysis of a complex 3D network. Scripting of the 3D viewer. Write an ImageJ macro to automatically segment the blood vessels and extract statistical information like the number of branching points, total length of the sub-networks and average thickness of the vessels.

Input data

3D stack of a subcutaneous tumor (lectin stained blood vessels) acquired with a custom SPIM.

Software

ImageJ Macro (ImageJ + Matlab) 0.8 : 0.2

Workflow

  1. Pre-processing
    • Tubeness filter to enhance tube-like objects
    • Grayscale morphological filtering (fill hollow tubes, ensure linking of weak segments)
  1. Segmentation
    • Local thresholding
    • 3D object analysis (connectivity –> sub-networks)
  1. Measurements
    • Skeletonization
    • Skeleton analysis –> branching points, network length, average thickness
  1. Visualization
    • Interaction with the 3D viewer to visualize the results
    • Optional: selection of sub-volumes for high resolution display (large dataset)
  1. Geodesic weight map (optional)
  2. Explain the concept of geodesic distance map
  3. Matlab: Find the minimum geodesic path between two points
  4. Application to filament tracing
  5. ImageJ simple neurite tracer plugin

Misc

Scheduled on: Day 4
Main Contributor: Sebastien

P9: Cell migration polarity 2: Automated Classification of Dynamics of Actin Cortex Components

Task

Cell migration is driven by polarized dynamics of the actin cytoskeleton and connected focal adhesion sites. In the following project, we learn how to classify detected image objects (focal adhesions) due to different object features (amongst others: actin flow above focal adhesions) by data mining techniques.

Input data

  1. 6min timeseries of a migrating cell in low temporal resolution. Focal adhesions (red) are imaged in epifluorescence
  2. 70s timeseries of the same migrating cell in high temporal resolution, imaged immediately after movie 1. Actin (green) is imaged in TIRF.

Software

ImageJ + Matlab (0.5 : 0.5)

Workflow

  1. Detecting of focal adhesions in imageJ:
    • Convolution & Filtering
    • Background Subtraction
    • Watershed segmentation
    • Export of mask images
  2. Measurement of actin flow with imageJ:
    • Quantification of actin flow in a timeseries by PIV analysis
    • Calculation of a mean actin flow velocity image
    • Export of the mean actin flow velocity image
  3. Measurement of focal adhesion object features with Matlab:
    • Import of focal adhesion mask images
    • Tracking of focal adhesion objects (simple assignment by spatial overlap of masks using bwlabel command)
    • Import of mean actin flow velocity image
    • Calculation of following features for each focal adhesion object:
      • mean actin flow velocity above focal adhesion
      • focal adhesion size
      • focal adhesion growth rate
  4. Classification of focal adhesions with Matlab:
    • Visualization of different focal adhesion classes with scatter plots in 2D parameter space
    • Classification in 2 classes by kmeans algorithm:
      1. Stable and large focal adhesions with strong actin coupling (=low actin flow)
      2. Dynamic and small focal adhesions with weak actin coupling (=high actin flow)
    • Plotting of focal adhesion object positions, color-coded by class, on original image data: Where are focal adhesion type 1 and type 2 located inside the cell?

important matlab functions

Colocalization Analysis

Task

Input data

Software

ImageJ Macro

Workflow

Misc

Main Contributor: Chong and Fabrice

Cell Tracking and Orientation Analysis

Task

Input data

Software

ImageJ Macro

Workflow

Misc

Main Contributor: JY and Kota