Tracking Cells and Their Lineages Via Labeled Random Finite Sets
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Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide. This paper proposes novel online algorithms for jointly tracking and resolving lineages of an unknown and time-varying number of cells from time-lapse video data. Our approach involves modeling the cell ensemble as a labeled random finite set with labels representing cell identities and lineages. A spawning model is developed to take into account cell lineages and changes in cell appearance prior to division. We then derive analytic filters to propagate multi-object distributions that contain information on the current cell ensemble including their lineages. We also develop numerical implementations of the resulting multi-object filters. Experiments using simulation, synthetic cell migration video, and real time-lapse sequence, are presented to demonstrate the capability of the solutions.
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Nguyen, Tran Thien Dat ; Shim, Changbeom ; Kim, W. (2021)Automatic cell tracking has long been a challenging problem due to the uncertainty of cell dynamic and observation process, where detection probability and clutter rate are unknown and time-varying. This is compounded ...
Dat Nguyen, T.; Kim, Du Yong (2018)© 2018 IEEE. In this paper, we propose an algorithm for tracking cells that also provides lineage information. Our approach incorporates cell spawning into the random finite set dynamic model of the cell population, which ...
Nguyen, Tran Thien Dat (2021)Cell tracking is an essential tool for studying how cells behave and divide under different conditions. This thesis proposes new approaches to track cells and their lineages using random finite set, which allows the ...