ilastik

ilastik[1] is a user-friendly free open source software for image classification and segmentation. No previous experience in image processing is required to run the software.

ilastik
Developer(s)Christoph Sommer, Christoph Straehle, Thorben Kröger, Bernhard X. Kausler, Ullrich Koethe, Fred A. Hamprecht and others
Initial release2011 (2011)
Stable release
1.3.3 / October 7, 2019 (2019-10-07)
Repository
Operating systemAny (Python based)
TypeImage processing & Computer vision & Machine Learning
LicenseGPL2
Websitewww.ilastik.org

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. ilastik has a CellProfiler module to use ilastik classifiers to process images within a CellProfiler framework.

History

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

Application
  • The Interactive Learning and Segmentation Toolkit
  • Carving[2][3]
  • Cell classification and neuron classification[4]
  • Synapse detection
  • Cell tracking[5]

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.

gollark: Okay, take a photograph of a single atom of your fingernail.
gollark: See, this is bad and very constrained.
gollark: Find two (3) apioform and photograph them.
gollark: I dare you to find two (3) apioform.
gollark: I am procrastinating on doing MAT revision by slightly improving the backup solution on my server.

References
  1. Sommer, C; Straehle C; Koethe U; Hamprecht FA (2011). ilastik: Interactive Learning and Segmentation Toolkit. IEEE International Symposium on Biomedical Imaging. pp. 230–33. doi:10.1109/ISBI.2011.5872394. ISBN 978-1-4244-4127-3.
  2. Straehle, C; Köthe U; Briggman K; Denk W; Hamprecht FA (2012). "Seeded watershed cut uncertainty estimators for guided interactive segmentation". CVPR.
  3. 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.
  4. 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. doi:10.1371/journal.pone.0024899. PMC 3198725. PMID 22031814.
  5. 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.
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