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Review
. 2019 Dec;16(12):1226-1232.
doi: 10.1038/s41592-019-0582-9. Epub 2019 Sep 30.

ilastik: interactive machine learning for (bio)image analysis

Stuart Berg  1 , Dominik Kutra  2   3 , Thorben Kroeger  2 , Christoph N Straehle  2 , Bernhard X Kausler  2 , Carsten Haubold  2 , Martin Schiegg  2 , Janez Ales  2 , Thorsten Beier  2 , Markus Rudy  2 , Kemal Eren  2 , Jaime I Cervantes  2 , Buote Xu  2 , Fynn Beuttenmueller  2   3 , Adrian Wolny  2 , Chong Zhang  2 , Ullrich Koethe  2 , Fred A Hamprecht  4 , Anna Kreshuk  5   6
Affiliations
Review

ilastik: interactive machine learning for (bio)image analysis

Stuart Berg et al. Nat Methods. 2019 Dec.

Abstract

We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.

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