Machine-learning-based image analysis with ilastik

Download workshop poster


ilastik is a free, open-source, and truly user-friendly image analysis tool that can help you leverage machine learning algorithms to easily segment, classify, track, and count your cells or other experimental data. Most operations are interactive, even on large, multi-dimensional datasets: you just draw the labels and immediately see the result. No machine learning expertise required. In this course you will learn how to perform automated pixel- and object-level segmentation, object classification, and tracking.

Participation will be limited to 20 people.
The format will be a combination of tutorials and hands-on exercises (you will need a laptop with a decent size screen and admin privileges).


This is a one-off workshop at HMS by Dominik Kutra, Senior Research Software Engineer in the Kreshuk Group at EMBL, Heidelberg, Germany:


Monday 17th June 2024, 9 AM to 5 PM (break from noon to 1 PM)


Harvard Medical School in Longwood, Boston, MA.




You should be curious about machine-learning based image analysis and bring a laptop with an external computer mouse. It would be ideal to install the latest beta version of ilastik before the start of the course: download.



  1. Introduction to machine-learning based image analysis
  2. Introduction to ilastik
  3. ilastik workflows:
    1. Pixel Classification
    2. Autocontext
    3. Neural Network Prediction


  1. ilastik workflows:
    1. Trainable Domain Adaptation
    2. Multicut
    3. Object Classification
    4. Tracking
  2. Automation
    1. Batch processing
    2. Fiji integration
    3. Command line usage
    4. Jupyter notebook usage


Free of charge.


Please apply for a seat by completing this form.
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Deadline for application:

Monday 3rd June 2024, 10 AM


Mostly based on your short motivation.
This is not a first-come first-serve event; we evaluate all applications after the deadline.
In case of over-subscription and multiple applicants from the same labs or departments, we will attempt to distribute the available seats evenly between the labs and departments.

Looking forward to see you there,
Dominik Kutra, Federico Gasparoli, Ranit Karmakar, Antoine Ruzette, Maria Theiss, and Simon F. Nørrelykke