@article {6334,
	title = {ilastik: interactive machine learning for (bio)image analysis},
	journal = {Nature Methods},
	volume = {16},
	year = {2019},
	pages = {1226-1232},
	doi = {10.1038/s41592-019-0582-9},
	author = {Stuart Berg and Kutra, D and Kroeger, T and Christoph N. Straehle and Bernhard X. Kausler and Haubold, C. and Schiegg, M and Ales, J and Thorsten Beier and Rudy, M and Eren, K and Cervantes, JI and Xu, B and Beuttenm{\"u}ller, F and Wolny, A and Zhang, C and Ullrich K{\"o}the and Fred A. Hamprecht and Kreshuk, A}
}
@mastersthesis {6937,
	title = {Tracking Dividing Cells Using Spatio-Temporal Embeddings},
	year = {2019},
	school = {Heidelberg University},
	author = {Xiao, S}
}
@article {6210,
	title = {An Objective Comparison of Cell Tracking Algorithms},
	journal = {Nature Methods},
	volume = {14},
	year = {2017},
	pages = {1141-1152},
	doi = {10.1038/NMETH.447},
	author = {Ulman, V. and Ma{\v s}ka, M. and Magnusson, K .E. G. and Ronneberger, O. and Haubold, C. and Harder, N. and Matula, P. and Matula, P. and Svoboda, D. and Radojevic, M. and Smal, I. and Karl Rohr and Jald{\'e}n, J. and Blau, H. M. and Dzyubachyk, O. and Lelieveldt, B. and Xiao, P. and Li, Y. and Cho, S-Y. and Dufour, A. and Olivo-Marin, J. C. and Reyes-Aldasoro, C. C. and Solis-Lemus, J. A. and Bensch, R. and Brox, T. and Stegmaier, J. and Mikut, R. and Wolf, S. and Fred A. Hamprecht and Esteves, T. and Quelhas, P. and Demirel, {\"O}. and Malstr{\"o}m, L. and Jug, F. and Toman{\v c}{\'a}k, P. and Meijering, E. and Mu{\~n}oz-Barrutia, A. and Kozubek, M. and Ortiz-de-Solorzano, C.}
}
@article {Mitra2015,
	title = {Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut},
	volume = {34},
	number = {7},
	year = {2015},
	abstract = {Figure-ground segmentation from bounding box input, provided either automatically or manually, has been ex-tremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practi-cal usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense CRF and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10{\texttimes}) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.},
	keywords = {I46 [IMAGE PROCESSING AND COMPUTER VISION], partitioning, Segmentation{\textemdash}Region growing},
	url = {http://mftp.mmcheng.net/Papers/DenseCut.pdf},
	author = {Mitra, N J and Stam, J and Xu, K and Cheng, Ming-Ming and Prisacariu, Victor Adrian and Zheng, Shuai and Torr, Philip H S and Carsten Rother}
}
@mastersthesis {xu_13_weakly,
	title = {Weakly supervised learning: Active schemes and partial annotations},
	year = {2013},
	school = {University of Heidelberg},
	author = {Xu, B.}
}
@article {renard_12_overcoming,
	title = {Overcoming species boundaries in peptide identification with BICEPS},
	journal = {Molecular and Cellular Proteomics},
	volume = {11},
	year = {2012},
	doi = {10.1074/mcp.M111.014167},
	author = {B. Y. Renard and Xu, B. and Kirchner, M. and Zickmann, F. and Winter, D. and Korten, S. and Brattig, N. and Tzur, A. and Fred A. Hamprecht and Steen, H.}
}
