A generalized labeled multi-Bernoulli tracker for time lapse cell migration
© 2017 IEEE. Tracking is a means to accomplish the more fundamental task of extracting relevant information about cell behavior from time-lapse microscopy data. Hence, characterizing uncertainty or confidence in the information inferred from the data is as important as the tracking of the cells. In...
| Main Authors: | , , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/66640 |
| _version_ | 1848761359318122496 |
|---|---|
| author | Kim, Du Yong Vo, Ba-Ngu Thian, A. Choi, Y. |
| author_facet | Kim, Du Yong Vo, Ba-Ngu Thian, A. Choi, Y. |
| author_sort | Kim, Du Yong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 IEEE. Tracking is a means to accomplish the more fundamental task of extracting relevant information about cell behavior from time-lapse microscopy data. Hence, characterizing uncertainty or confidence in the information inferred from the data is as important as the tracking of the cells. In this paper, we show that in addition to being a principled Bayesian multi-object tracking approach, the Random Finite Set (RFS) framework is capable of providing consistent characterization of uncertainty for the information inferred from the data. In particular, we use an efficient implementation of the Generalized Labeled Multi-Bernoulli (GLMB) filter to track a large number of cells in a cell migration experiment and demonstrate how to characterize uncertainty on variables inferred from the data such as cell counts, survival rate, birth rate, mean position, mean velocity using standard constructs from RFS theory. |
| first_indexed | 2025-11-14T10:30:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-66640 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:30:25Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-666402022-10-27T06:22:50Z A generalized labeled multi-Bernoulli tracker for time lapse cell migration Kim, Du Yong Vo, Ba-Ngu Thian, A. Choi, Y. © 2017 IEEE. Tracking is a means to accomplish the more fundamental task of extracting relevant information about cell behavior from time-lapse microscopy data. Hence, characterizing uncertainty or confidence in the information inferred from the data is as important as the tracking of the cells. In this paper, we show that in addition to being a principled Bayesian multi-object tracking approach, the Random Finite Set (RFS) framework is capable of providing consistent characterization of uncertainty for the information inferred from the data. In particular, we use an efficient implementation of the Generalized Labeled Multi-Bernoulli (GLMB) filter to track a large number of cells in a cell migration experiment and demonstrate how to characterize uncertainty on variables inferred from the data such as cell counts, survival rate, birth rate, mean position, mean velocity using standard constructs from RFS theory. 2017 Conference Paper http://hdl.handle.net/20.500.11937/66640 10.1109/ICCAIS.2017.8217576 http://purl.org/au-research/grants/arc/DP160104662 restricted |
| spellingShingle | Kim, Du Yong Vo, Ba-Ngu Thian, A. Choi, Y. A generalized labeled multi-Bernoulli tracker for time lapse cell migration |
| title | A generalized labeled multi-Bernoulli tracker for time lapse cell migration |
| title_full | A generalized labeled multi-Bernoulli tracker for time lapse cell migration |
| title_fullStr | A generalized labeled multi-Bernoulli tracker for time lapse cell migration |
| title_full_unstemmed | A generalized labeled multi-Bernoulli tracker for time lapse cell migration |
| title_short | A generalized labeled multi-Bernoulli tracker for time lapse cell migration |
| title_sort | generalized labeled multi-bernoulli tracker for time lapse cell migration |
| url | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/66640 |