@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 {47511,
	title = {Segmenting and Tracking Multiple Dividing Targets Using ilastik.},
	journal = {Advances in anatomy, embryology, and cell biology},
	volume = {219},
	year = {2016},
	month = {2016 May 21},
	pages = {199-229},
	abstract = {<p>Tracking crowded cells or other targets in biology is often a challenging task due to poor signal-to-noise ratio, mutual occlusion, large displacements, little discernibility, and the ability of cells to divide. We here present an open source implementation of conservation tracking (Schiegg et\&nbsp;al., IEEE international conference on computer vision (ICCV). IEEE, New York, pp\&nbsp;2928-2935, 2013) in the ilastik software framework. This robust tracking-by-assignment algorithm explicitly makes allowance for false positive detections, undersegmentation, and cell division. We give an overview over the underlying algorithm and parameters, and explain the use for a light sheet microscopy sequence of a Drosophila embryo. Equipped with this knowledge, users will be able to track targets of interest in their own data.</p>
},
	issn = {0301-5556},
	doi = {10.1007/978-3-319-28549-8_8},
	author = {Haubold, Carsten and Schiegg, Martin and Kreshuk, Anna and Berg, Stuart and Koethe, Ullrich and Hamprecht, Fred A}
}
