Ilastik

Last updated
ilastik
Developer(s) Christoph Sommer, Christoph Straehle, Thorben Kröger, Bernhard X. Kausler, Ullrich Koethe, Fred A. Hamprecht, Anna Kreshuk, Dominik Kutra, Stuart Berg, Benedikt Best, Maksim Novikov, Emil Melnikov, Tomaz Vieira and others
Initial release2011;14 years ago (2011)
Stable release
1.4.1 / May 31, 2025;30 days ago (2025-05-31)
Repository
Operating system Any (Python based)
Type Image processing & Computer vision & Machine Learning
License GPL2
Website www.ilastik.org

ilastik [1] is free open source software for image classification and segmentation. No previous experience in image processing is required to run the software. Since 2018 ilastik is further developed and maintained by Anna Kreshuk's group at European Molecular Biology Laboratory.

Contents

Features

ilastik allows user to annotate an arbitrary number of classes in images with a mouse interface. Using these user annotations and the generic (nonlinear) image features, the user can train a random forest classifier. Trained ilastik classifiers can be applied new data not included in the training set in ilastik via its batch processing functionality, [2] or without using the graphical user interface, in headless mode. [3] ilastik can be integrated into various related tools:

History

ilastik was first released in 2011 by scientists at the Heidelberg Collaboratory for Image Processing (HCI), University of Heidelberg.

Application

Resources

ilastik project is hosted on GitHub. It is a collaborative project, any contributions such as comments, bug reports, bug fixes or code contributions are welcome. The ilastik team can be contacted for user support on the image.sc forum.

References

  1. ↑ Sommer, C; Straehle C; Koethe U; Hamprecht FA (2011). "Ilastik: Interactive learning and segmentation toolkit". 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. pp. 230–33. doi:10.1109/ISBI.2011.5872394. ISBN   978-1-4244-4127-3. S2CID   206949135.
  2. ↑ "ilastik batch processing documentation". ilastik.org. Retrieved 30 April 2024.
  3. ↑ "ilastik headless mode documentation". ilastik.org. Retrieved 30 April 2024.
  4. ↑ "ilastik batch ImageJ plugin documentation". ilastik ImageJ plugin on github. Retrieved 30 April 2024.
  5. ↑ "ilastik Python API example". ilastik github pixel classification api notebook. Retrieved 30 April 2024.
  6. ↑ Straehle, C; Köthe U; Briggman K; Denk W; Hamprecht FA (2012). "Seeded watershed cut uncertainty estimators for guided interactive segmentation". CVPR.
  7. ↑ Straehle, CN; Köthe U; Knott G; Hamprecht FA (2011). "Carving: scalable interactive segmentation of neural volume electron microscopy images". MICCAI. 14 (Pt 1): 653–60. doi: 10.1007/978-3-642-23623-5_82 . PMID   22003674.
  8. ↑ Kreshuk, A; Straehle CN; Sommer C; Koethe U; Cantoni M; et al. (2011). "Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images". PLOS ONE. 6 (10): e24899. Bibcode:2011PLoSO...624899K. doi: 10.1371/journal.pone.0024899 . PMC   3198725 . PMID   22031814.
  9. ↑ Berg, Stuart; Kutra, Dominik; Kroeger, Thorben; Straehle, Christoph N.; Kausler, Bernhard X.; Haubold, Carsten; Schiegg, Martin; Ales, Janez; Beier, Thorsten; Rudy, Markus; Eren, Kemal; Cervantes, Jaime I; Xu, Buote; Beuttenmueller, Fynn; Wolny, Adrian; Zhang, Chong; Koethe, Ullrich; Hamprecht, Fred A.; Kreshuk, Anna (30 September 2019). "ilastik: interactive machine learning for (bio)image analysis". Nature Methods. 16 (12): 1226–1232. doi:10.1038/s41592-019-0582-9. PMID   31570887. S2CID   203609613.
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