@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 {48254,
	title = {Multicut brings automated neurite segmentation closer to human performance.},
	journal = {Nature Methods},
	volume = {14},
	year = {2017},
	month = {2017 Jan 31},
	pages = {101-102},
	issn = {1548-7105},
	doi = {10.1038/nmeth.4151},
	author = {Beier, Thorsten and Pape, Constantin and Rahaman, Nasim and Prange, Timo and Berg, Stuart and Bock, Davi D and Cardona, Albert and Knott, Graham W and Plaza, Stephen M and Scheffer, Louis K and Koethe, Ullrich and Kreshuk, Anna and Hamprecht, Fred A}
}
@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}
}
@article {6880,
	title = {Automated detection of synapses in serial section transmission electron microscopy image stacks.},
	journal = {PloS one},
	volume = {9},
	year = {2014},
	month = {2014},
	pages = {e87351},
	abstract = {We describe a method for fully automated detection of chemical synapses in serial electron microscopy images with highly anisotropic axial and lateral resolution, such as images taken on transmission electron microscopes. Our pipeline starts from classification of the pixels based on 3D pixel features, which is followed by segmentation with an Ising model MRF and another classification step, based on object-level features. Classifiers are learned on sparse user labels; a fully annotated data subvolume is not required for training. The algorithm was validated on a set of 238 synapses in 20 serial 7197{\texttimes}7351 pixel images (4.5{\texttimes}4.5{\texttimes}45 nm resolution) of mouse visual cortex, manually labeled by three independent human annotators and additionally re-verified by an expert neuroscientist. The error rate of the algorithm (12\% false negative, 7\% false positive detections) is better than state-of-the-art, even though, unlike the state-of-the-art method, our algorithm does not require a prior segmentation of the image volume into cells. The software is based on the ilastik learning and segmentation toolkit and the vigra image processing library and is freely available on our website, along with the test data and gold standard annotations (http://www.ilastik.org/synapse-detection/sstem).},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0087351},
	author = {Kreshuk, Anna and Koethe, Ullrich and Pax, Elizabeth and Bock, Davi D and Hamprecht, Fred A}
}
