Workflow

A workflow is a set of components assembled in some specific order to

  1. Measure and estimate some numerical parameters of the biological system or
  2. Visualization

for addressing a biological question. Workflows can be a combination of components from the same or different software packages using several scripts and manual steps.

Introduction to ImageJ macro language

Submitted by gaby on Wed, 10/17/2018 - 19:41

In this session, we will cover the basics of ImageJ macro programming using a simple example: how to quantify signal enrichment at the nuclear rim? Trainees will (re)discover how to record actions, plan a workflow and organise their code. This session will alternate presentation of technical points, to be directly applied during practical exercises. The macro will progressively complexify as new notions are taught.

2D Gaussian fitting macro (Fiji/ImageJ) for multiple signals.

Workflow
Description

This script includes a rough feature detection and then fine 2D Gaussian algorithm to fit Gaussians within detected regions. This macro is unique because the ImageJ/Fiji curve fitting API only supports 1-D curve. I get around this by linearising the equation. This implementation is for isotropic (spherical) or anistropic (longer in x/y) diagonally covariant Gaussians but not fully covariant Gaussians (anisotropic and rotated). 

has topic
Bioimage informatics
Single molecule localization microscopy
has function
Image analysis
Single molecule localisation

ZEN and APEER Machine Learning Platform

Collection
Component
Workflow
Description

ZEN and APEER – Open Ecosystem for integrated Machine-Learning Workflows

Open ecosystem for integrated machine-learning workflows to train and use machine-learning models for image processing and image analysis inside the ZEN software or on the APEER cloud-based platform

Highlights ZEN

  • Simple User Interface for Labeling and Training
  • Engineered Features Sets and Deep Feature Extraction + Random Forrest for Semantic Segmentation
  • Object Classification workflows
  • Probability Thresholds and Conditional Random Fields
  • Import your own trained models as *.czann files (see: czmodel · PyPI)
  • Import "AIModel Containes" from arivis AI for advanced Instance Segmentation
  • Integration into ZEN Measurement Framework
  • Support for Multi-dimensional Datasets and Tile Images
  • open and standardized format to store trained models
ZEN Intellesis Segmentation

ZEN Intellesis Segmentation - Training UI

ZEN Intellesis - Pretrained Networks

ZEN Intellesis Segmentation - Use Deep Neural Networks

Intellesis Object Classification

ZEN Object Classification

Highlights Aarivis AI

  • Web-based tool to label datasets to train Deep Neural Networks
  • Fully automated hyper-parameter tuning
  • Export of trained models for semantic segmentation and AIModelContainer for Instance Segmentation
Annotation Tool

APEER Annotation Tool

has topic
Digital histology
High content screening
Electron microscopy
Light microscopy
Fluorescence microscopy
Machine learning
Supervised learning
has function
Image segmentation
Cell segmentation
Model-based segmentation
Pixel classification
Object classification
Image annotation

Fit a model for the growth of yeast cells

Workflow
Description

This notebook uses the rOMERO-gateway and EBImage to process an Image associated to the paper 'Timing of gene expression in a cell-fate decision system'.

The Image "Pos22" is taken from the dataset idr0040-aymoz-singlecell/experimentA/YDA306_AGA1y_PRM1r_Mating. It is a timelapse Image with 42 timepoints separated by 5 minutes. This Image is used to fit a model for the growth of the yeast cells. The notebook does not replicate any of the analysis of the above mentioned paper.

Its purpose is mainly to demonstrate the use of Jupyter, rOMERO-gateway and EBimage.

 

What it does:

  • For each time point of one movie:
    • Read the image for this time point  from the IDR
    • Threshold the images and count the cells using EBimage functions
  • Fit an exponential model to the count of cells against time to get a coefficient of grow (exponential factor)

 

 

 

has topic
High content screening
Fluorescence microscopy
has function
Analysis
Object counting
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