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  Efficient Algorithms for Moral Lineage Tracing

Rempfler, M., Lange, J.-H., Jug, F., Blasse, C., Myers, E. W., Menze, B. H., et al. (2017). Efficient Algorithms for Moral Lineage Tracing. Retrieved from http://arxiv.org/abs/1702.04111.

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arXiv:1702.04111.pdf (Preprint), 839KB
 
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 Urheber:
Rempfler, Markus1, Autor
Lange, Jan-Hendrik2, Autor           
Jug, Florian1, Autor
Blasse, Corinna1, Autor
Myers, Eugene W.1, Autor
Menze, Bjoern H.1, Autor
Andres, Björn2, Autor           
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              

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Schlagwörter: Computer Science, Computer Vision and Pattern Recognition, cs.CV
 Zusammenfassung: Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task. Jug et al. have proposed a mathematical abstraction of this task, the moral lineage tracing problem (MLTP) whose feasible solutions define a segmentation of every image and a lineage forest of cells. Their branch-and-cut algorithm, however, is prone to many cuts and slow convergences for large instances. To address this problem, we make three contributions: Firstly, we improve the branch-and-cut algorithm by separating tighter cutting planes. Secondly, we define two primal feasible local search algorithms for the MLTP. Thirdly, we show in experiments that our algorithms decrease the runtime on the problem instances of Jug et al. considerably and find solutions on larger instances in reasonable time.

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 Datum: 2017-02-142017
 Publikationsstatus: Online veröffentlicht
 Seiten: 9 p.
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 Identifikatoren: arXiv: 1702.04111
URI: http://arxiv.org/abs/1702.04111
BibTex Citekey: rempfler-2017-arxiv
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