Biological cell tracking and lineage inference via random finite sets
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 when cell lineages are also to be inferred. In this paper, we propose a novel biol...
| Main Authors: | , , |
|---|---|
| Format: | Conference Paper |
| Language: | English |
| Published: |
IEEE
2021
|
| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/93471 |
| _version_ | 1848765738461954048 |
|---|---|
| author | Nguyen, Tran Thien Dat Shim, Changbeom Kim, W. |
| author_facet | Nguyen, Tran Thien Dat Shim, Changbeom Kim, W. |
| author_sort | Nguyen, Tran Thien Dat |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 when cell lineages are also to be inferred. In this paper, we propose a novel biological cell tracking method based on the Labeled Random Finite Set (RFS) approach to study cell migration patterns. Our method tracks cells with lineage by using a Generalised Label Multi-Bernoulli (GLMB) filter with objects spawning, and a robust Cardinalised Probability Hypothesis Density (CPHD) to address unknown and time-varying detection probability and clutter rate. The proposed method is capable of quantifying the certainty level of the tracking solutions. The capability of the algorithm on population dynamic inference is demonstrated on a migration sequence of breast cancer cells. |
| first_indexed | 2025-11-14T11:40:01Z |
| format | Conference Paper |
| id | curtin-20.500.11937-93471 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:40:01Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-934712023-11-07T05:21:43Z Biological cell tracking and lineage inference via random finite sets Nguyen, Tran Thien Dat Shim, Changbeom Kim, W. Science & Technology Technology Life Sciences & Biomedicine Engineering, Biomedical Radiology, Nuclear Medicine & Medical Imaging Engineering Cell Tracking Cell Lineage Inference Track-By-Detection Random Finite Set 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 when cell lineages are also to be inferred. In this paper, we propose a novel biological cell tracking method based on the Labeled Random Finite Set (RFS) approach to study cell migration patterns. Our method tracks cells with lineage by using a Generalised Label Multi-Bernoulli (GLMB) filter with objects spawning, and a robust Cardinalised Probability Hypothesis Density (CPHD) to address unknown and time-varying detection probability and clutter rate. The proposed method is capable of quantifying the certainty level of the tracking solutions. The capability of the algorithm on population dynamic inference is demonstrated on a migration sequence of breast cancer cells. 2021 Conference Paper http://hdl.handle.net/20.500.11937/93471 10.1109/ISBI48211.2021.9433957 English http://purl.org/au-research/grants/arc/DP160104662 IEEE fulltext |
| spellingShingle | Science & Technology Technology Life Sciences & Biomedicine Engineering, Biomedical Radiology, Nuclear Medicine & Medical Imaging Engineering Cell Tracking Cell Lineage Inference Track-By-Detection Random Finite Set Nguyen, Tran Thien Dat Shim, Changbeom Kim, W. Biological cell tracking and lineage inference via random finite sets |
| title | Biological cell tracking and lineage inference via random finite sets |
| title_full | Biological cell tracking and lineage inference via random finite sets |
| title_fullStr | Biological cell tracking and lineage inference via random finite sets |
| title_full_unstemmed | Biological cell tracking and lineage inference via random finite sets |
| title_short | Biological cell tracking and lineage inference via random finite sets |
| title_sort | biological cell tracking and lineage inference via random finite sets |
| topic | Science & Technology Technology Life Sciences & Biomedicine Engineering, Biomedical Radiology, Nuclear Medicine & Medical Imaging Engineering Cell Tracking Cell Lineage Inference Track-By-Detection Random Finite Set |
| url | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/93471 |