The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Ilastik: Interactive learning and segmentation toolkit
Abstract
Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org.
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
Cited by
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Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021) · Nature Methods · doi:10.1038/s41592-020-01018-x · Cited by 4,369
Cell Detection with Star-Convex Polygons
Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00934-2_30 · Cited by 1,826
Finotello, F., Mayer, C., Plattner, C., Laschober, G., Rieder, D., Hackl, H., Krogsdam, A., Loncova, Z., Posch, W., Wilflingseder, D., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0638-6 · Cited by 1,542
Deep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225
Mapping of Brain Activity by Automated Volume Analysis of Immediate Early Genes
Renier, N., Adams, E. L., Kirst, C., Wu, Z., Azevedo, R., Kohl, J., Autry, A. E., Kadiri, L., Umadevi Venkataraju, K., Zhou, Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.05.007 · Cited by 918
Metadata
- Authors
- Sommer, C., Straehle, C., Kothe, U., & Hamprecht, F. A.
- Venue
- 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
- Publisher
- IEEE
- Published
- 2011-03
- Pages
- 230-233
- OpenAlex
- W2160041854
- Cited by
- 1,125
- References
- 2
- FWCI
- 104.76
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Sommer, C., Straehle, C., Kothe, U., & Hamprecht, F. A. (2011). Ilastik: Interactive learning and segmentation toolkit. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. https://doi.org/10.1109/isbi.2011.5872394
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