Graphical model for joint segmentation and tracking of multiple dividing cells

To gain fundamental insight into the development of embryos, biologists seek to understand the fate of each and every embryonic cell. For the generation of cell tracks in embryogenesis, so-called tracking-by-assignment methods are flexible approaches. However, as every two-stage approach, they suffe...

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Bibliographic Details
Published in:Bioinformatics 2015-03, Vol.31 (6), p.948-956
Main Authors: Schiegg, Martin, Hanslovsky, Philipp, Haubold, Carsten, Koethe, Ullrich, Hufnagel, Lars, Hamprecht, Fred A
Format: Article
Language:English
Subjects:
Algorithms
Animals
Bioinformatics
Cell Division
Cell Nucleus
Drosophila
Drosophila - cytology
Drosophila - embryology
Embryo, Nonmammalian - ultrastructure
Embryos
Imaging, Three-Dimensional - methods
Manuals
Models, Statistical
Segmentation
Source code
Tracking
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Summary:To gain fundamental insight into the development of embryos, biologists seek to understand the fate of each and every embryonic cell. For the generation of cell tracks in embryogenesis, so-called tracking-by-assignment methods are flexible approaches. However, as every two-stage approach, they suffer from irrevocable errors propagated from the first stage to the second stage, here from segmentation to tracking. It is therefore desirable to model segmentation and tracking in a joint holistic assignment framework allowing the two stages to maximally benefit from each other. We propose a probabilistic graphical model, which both automatically selects the best segments from a time series of oversegmented images/volumes and links them across time. This is realized by introducing intra-frame and inter-frame constraints between conflicting segmentation and tracking hypotheses while at the same time allowing for cell division. We show the efficiency of our algorithm on a challenging 3D+t cell tracking dataset from Drosophila embryogenesis and on a 2D+t dataset of proliferating cells in a dense population with frequent overlaps. On the latter, we achieve results significantly better than state-of-the-art tracking methods. Source code and the 3D+t Drosophila dataset along with our manual annotations will be freely available on http://hci.iwr.uni-heidelberg.de/MIP/Research/tracking/
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btu764