Ilastik
| Developer(s) | Christoph Sommer, Christoph Straehle, Thorben Kröger, Bernhard X. Kausler, Ullrich Koethe, Fred A. Hamprecht and others |
|---|---|
| Initial release | 2011 |
| Stable release | 1.1.7
/ October 9, 2015 |
| 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.
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
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.
References
- ^ Sommer, C; Straehle C; Koethe U; Hamprecht FA (2011). "ilastik: Interactive Learning and Segmentation Toolkit". IEEE International Symposium on Biomedical Imaging: 230–33. doi:10.1109/ISBI.2011.5872394.
- ^ Straehl, 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". Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images. 6 (10): e24899. doi:10.1371/journal.pone.0024899. PMC 3198725. PMID 22031814.
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