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Efficient Algorithms for Moral Lineage Tracing

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons201523

Lange,  Jan-Hendrik
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons98382

Andres,  Bjoern
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1702.04111.pdf
(Preprint), 839KB

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-7960-1
Abstract
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.