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 |
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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 |
| _version_ | 1848765430777249792 |
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| author | Nguyen, Tran Thien Dat Vo, Ba-Ngu Vo, Ba Tuong Kim, Du Yong Choi, Y.S. |
| author_facet | Nguyen, Tran Thien Dat Vo, Ba-Ngu Vo, Ba Tuong Kim, Du Yong Choi, Y.S. |
| author_sort | Nguyen, Tran Thien Dat |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:35:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-90796 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:08Z |
| publishDate | 2021 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-907962023-04-24T01:45:14Z Tracking Cells and Their Lineages Via Labeled Random Finite Sets Nguyen, Tran Thien Dat Vo, Ba-Ngu Vo, Ba Tuong Kim, Du Yong Choi, Y.S. Science & Technology Technology Engineering, Electrical & Electronic Engineering Licenses Cell tracking lineages inference Random Finite Sets multi-object tracking MICROSCOPY IMAGE SEQUENCES MULTIPLE OBJECT TRACKING MITOSIS DETECTION MULTITARGET TRACKING ALGORITHM SEGMENTATION POPULATIONS ASSIGNMENTS FRAMEWORK RANKING 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. 2021 Journal Article http://hdl.handle.net/20.500.11937/90796 10.1109/TSP.2021.3111705 English http://purl.org/au-research/grants/arc/DP160104662 http://creativecommons.org/licenses/by/4.0/ IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC fulltext |
| spellingShingle | Science & Technology Technology Engineering, Electrical & Electronic Engineering Licenses Cell tracking lineages inference Random Finite Sets multi-object tracking MICROSCOPY IMAGE SEQUENCES MULTIPLE OBJECT TRACKING MITOSIS DETECTION MULTITARGET TRACKING ALGORITHM SEGMENTATION POPULATIONS ASSIGNMENTS FRAMEWORK RANKING Nguyen, Tran Thien Dat Vo, Ba-Ngu Vo, Ba Tuong Kim, Du Yong Choi, Y.S. Tracking Cells and Their Lineages Via Labeled Random Finite Sets |
| title | Tracking Cells and Their Lineages Via Labeled Random Finite Sets |
| title_full | Tracking Cells and Their Lineages Via Labeled Random Finite Sets |
| title_fullStr | Tracking Cells and Their Lineages Via Labeled Random Finite Sets |
| title_full_unstemmed | Tracking Cells and Their Lineages Via Labeled Random Finite Sets |
| title_short | Tracking Cells and Their Lineages Via Labeled Random Finite Sets |
| title_sort | tracking cells and their lineages via labeled random finite sets |
| topic | Science & Technology Technology Engineering, Electrical & Electronic Engineering Licenses Cell tracking lineages inference Random Finite Sets multi-object tracking MICROSCOPY IMAGE SEQUENCES MULTIPLE OBJECT TRACKING MITOSIS DETECTION MULTITARGET TRACKING ALGORITHM SEGMENTATION POPULATIONS ASSIGNMENTS FRAMEWORK RANKING |
| url | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/90796 |