@article {65178,
	title = {ilastik: interactive machine learning for (bio)image analysis.},
	journal = {Nature Methods},
	volume = {16},
	year = {2019},
	month = {2019 Sep 30},
	pages = {1226-32},
	abstract = {<p>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.</p>
},
	issn = {1548-7105},
	doi = {10.1038/s41592-019-0582-9},
	author = {Berg, Stuart and Kutra, Dominik and Kroeger, Thorben and Straehle, Christoph N and Kausler, Bernhard X and Haubold, Carsten and Schiegg, Martin and Ales, Janez and Beier, Thorsten and Rudy, Markus and Eren, Kemal and Cervantes, Jaime I and Xu, Buote and Beuttenmueller, Fynn and Wolny, Adrian and Zhang, Chong and Koethe, Ullrich and Hamprecht, Fred A and Kreshuk, Anna}
}
@article {48647,
	title = {The candidate multi-cut for cell segmentation.},
	journal = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)},
	year = {2017},
	month = {2017 Jul 04},
	abstract = {<p>Two successful approaches for the segmentation of biomedical images are (1) the selection of segment candidates from a merge-tree, and (2) the clustering of small superpixels by solving a Multi-Cut problem. In this paper, we introduce a model that unifies both approaches. Our model, the Candidate Multi-Cut (CMC), allows joint selection and clustering of segment candidates from a merge-tree. This way, we overcome the respective limitations of the individual methods: (1) the space of possible segmentations is not constrained to candidates of a merge-tree, and (2) the decision for clustering can be made on candidates larger than superpixels, using features over larger contexts. We solve the optimization problem of selecting and clustering of candidates using an integer linear program. On datasets of 2D light microscopy of cell populations and 3D electron microscopy of neurons, we show that our method generalizes well and generates more accurate segmentations than merge-tree or Multi-Cut methods alone.</p>
},
	doi = {10.1109/ISBI.2018.8363658},
	author = {Funke, Jan and Zhang, Chong and Pietzsch, Tobias and Gonzalez Ballester, Miguel A and Saalfeld, Stephan}
}
