ilastik: interactive machine learning for (bio)image analysis
Top Cited Papers
- 30 September 2019
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Methods
- Vol. 16 (12), 1226-1232
- https://doi.org/10.1038/s41592-019-0582-9
Abstract
We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.Keywords
Funding Information
- Deutsche Forschungsgemeinschaft (KR-4496/1-1, HA 4364 11-1, HA 4364 10-1, HA 4364 9-1, SFB 1129, CellNetworks, FOR 2581)
- HHMI Janelia Research Campus, Visiting Scientist Program
- internal funding
- European Commission (HBP SGA1, HBP SGA1)
- HHMI Janelia Research Campus Visiting Scientist Program
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