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ilastik

ilastik is an interactive machine learning toolkit that enables users to classify pixels, segment images, track objects, and count cells without requiring machine learning expertise.

Overview

The ilastik environment provides a user-friendly interface for interactive machine learning-based image analysis, making advanced segmentation and classification accessible to biologists and researchers.

Environment

Environment: ilastik

Available Tasks

Start ilastik GUI

Launch the ilastik graphical user interface:

pixi run ilastik

Features

  • Interactive machine learning: Train classifiers by providing examples
  • Pixel classification: Classify pixels based on their local appearance
  • Object classification: Classify segmented objects based on their features
  • Carving workflow: Semi-automatic segmentation for challenging objects
  • Tracking: Track objects across time-lapse sequences
  • Counting: Automated object counting in images
  • Batch processing: Process multiple images with trained classifiers
  • Feature extraction: Comprehensive set of image features for analysis

Workflows Available

Pixel Classification

Train a classifier to distinguish different tissue types, cell types, or structures at the pixel level.

Object Classification

Segment objects first, then classify them based on shape, intensity, and other features.

Autocontext

Improve classification results by using predictions as additional input features.

Carving

Interactive segmentation tool for separating touching or overlapping objects.

Tracking

Track objects through time-lapse sequences with manual correction capabilities.

Counting

Count objects in images using density estimation or detection-based approaches.

Getting Started

  1. Make sure you have pixi installed and this repository cloned
  2. Navigate to the repository directory
  3. Run pixi run ilastik to start the ilastik interface
  4. Choose your desired workflow and start training your classifier!

Use Cases

  • Cell segmentation: Separate cells from background and from each other
  • Tissue classification: Classify different tissue types in histological images
  • Organelle detection: Identify and classify cellular organelles
  • Quality control: Classify images based on quality metrics
  • Time-lapse analysis: Track cell division, migration, and other dynamic processes

Resources