Reliable Cell Tracking by Global Data Association
Automated cell tracking in populations is important for research and discovery in biology and medicine. In this paper, we propose a cell tracking method based on global spatio-temporal data association which considers hypotheses of initialization, termination, translation, division and false positive in an integrated formulation. Firstly, reliable tracklets (i.e., short trajectories) are generated by linking detection responses based on frame-by-frame association. Next, these tracklets are globally associated over time to obtain final cell trajectories and lineage trees. During global association, tracklets form tree structures where a mother cell divides into two daughter cells. We formulate the global association for tree structures as a maximum-a-posteriori (MAP) problem and solve it by linear programming. This approach is quantitatively evaluated on sequences with thousands of cells captured over several days.
R. Bise et al., "Reliable Cell Tracking by Global Data Association," Proceedings of the International Symposium on Biomedical Imaging (2011, Chicago, IL), pp. 1004-1010, Institute of Electrical and Electronics Engineers (IEEE), Mar 2011.
The definitive version is available at https://doi.org/10.1109/ISBI.2011.5872571
International Symposium on Biomedical Imaging (2011: Mar. 30-Apr. 2, Chicago, IL)
Keywords and Phrases
Automated Cell; Biology and Medicine; Cell Tracking; Daughter Cells; False Positive; Global Data; Maximum a Posteriori; Spatio-Temporal Data; Tree Structures; Medical Imaging; Plant Extracts; Population Statistics; Trees (Mathematics); Cells; Global Data Association
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Article - Conference proceedings
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