ilastik
| Developer(s) | Christoph Sommer, Christoph Straehle, Thorben Kröger, Bernhard X. Kausler, Ullrich Koethe, Fred A. Hamprecht, Anna Kreshuk and others |
|---|---|
| Initial release | 2011 |
| Stable release | 1.4.0
/ November 10, 2023 |
| Repository | |
| Operating system | Any (Python based) |
| Type | Image processing & Computer vision & Machine Learning |
| License | GPL2 |
| Website | www |
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. Since 2018 ilastik is further developed and maintaned by Anna Kreshuk's group at European Molecular Biology Laboratory.
Features[edit]
ilastik allows user to annotate an arbitrary number of classes in images with a mouse interface. Using these user annotations and the generic (nonlinear[disambiguation needed]) 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 it's batch processing functionality[2], or without using the graphical user interface, in headless mode[3]. Furthermore, ilastik can be integrated into various related tools:
- Pre-trained workflows can be executed directly from ImageJ/Fiji using the ilastik-ImageJ plugin[4].
- Pre-trained ilastik Pixel Classification workflows can be run directly in Python with the ilastik Python package[5], which is available via conda.
- ilastik has a CellProfiler module to use ilastik classifiers to process images within a CellProfiler framework.
History[edit]
ilastik was first released in 2011 by scientists at the Heidelberg Collaboratory for Image Processing (HCI), University of Heidelberg.
Application[edit]
- The Interactive Learning and Segmentation Toolkit
- Carving[6][7]
- Cell classification and neuron classification[8]
- Synapse detection
- Cell tracking[9]
- Neural Network Classification
Resources[edit]
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[edit]
- ^ 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.
- ^ "ilastik batch processing documentation". ilastik.org. Retrieved 30 April 2024.
- ^ "ilastik headless mode documentation". ilastik.org. Retrieved 30 April 2024.
- ^ "ilastik batch ImageJ plugin documentation". ilastik ImageJ plugin on github. Retrieved 30 April 2024.
- ^ "ilastik Python API example". ilastik github pixel classification api notebook. Retrieved 30 April 2024.
- ^ Straehle, C; Köthe U; Briggman K; Denk W; Hamprecht FA (2012). "Seeded watershed cut uncertainty estimators for guided interactive segmentation". CVPR.
- ^ 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.
- ^ 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.
- ^ 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.
