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ilastik: interactive machine learning for (bio)image analysis


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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Fig. 1: User labels provided to various ilastik workflows and the corresponding ilastik output.
Fig. 2: Nuclei segmentation.
Fig. 3: A combination of pixel and object classification workflows.
Fig. 4: Segmentation of the peripheral endoplasmic reticulum from FIB–SEM image stacks by the carving workflow.


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We gratefully acknowledge support by the HHMI Janelia Visiting Scientist Program, European Union via the Human Brain Project SGA2, the Deutsche Forschungsgemeinschaft (DFG) under grants HA-4364/11-1 (F.A.H., A.K.), HA 4364 9-1 (F.A.H.), HA 4364 10-1 (F.A.H.), KR-4496/1-1 (A.K.), SFB1129 (F.A.H.), FOR 2581 (F.A.H.), and the Heidelberg Graduate School MathComp. We are also extremely grateful to other contributors to ilastik: N. Buwen, C. Decker, B. Erocal, L. Fiaschi, T. Fogaca Vieira, P. Hanslovsky, B. Heuer, P. Hilt, G. Holst, F. Isensee, K. Karius, J. Kleesiek, E. Melnikov, M. Novikov, M. Nullmeier, L. Parcalabescu, O. Petra and S. Wolf, and to B. Werner for vital assistance to the project. Finally, we would like to thank the authors of the three case studies for sharing their images with us.

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S.B., D.K., T.K., C.N.S., B.X.K., C.H., M.S., J.A., T.B., M.R., K.E., J.I.C., B.X., F.B., A.W., C.Z., U.K, F.A.H. and A.K. all contributed to the software code and documentation. A.K. and F.A.H. drafted the manuscript, to which all authors contributed.

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Correspondence to Fred A. Hamprecht or Anna Kreshuk.

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The authors declare no competing interests.

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Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Berg, S., Kutra, D., Kroeger, T. et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods 16, 1226–1232 (2019).

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