Tracking Cells and Their Lineages Via Labeled Random Finite Sets
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...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2021
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/90796 |
| Summary: | 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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