期刊
NATURE METHODS
卷 16, 期 12, 页码 1226-1232出版社
NATURE PORTFOLIO
DOI: 10.1038/s41592-019-0582-9
关键词
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资金
- HHMI Janelia Visiting Scientist Program
- European Union via the Human Brain Project SGA2
- Deutsche Forschungsgemeinschaft (DFG) [HA-4364/11-1, HA 4364 9-1, HA 4364 10-1, KR-4496/1-1, SFB1129, FOR 2581]
- Heidelberg Graduate School MathComp
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 non-linear 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.
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