Abstract
Automated tracking of cell population is an important element of research and discovery in the biology field. In this paper, we propose a method that tracks cells under highly confluent conditions by using the candidate cell region detection-based association approach. Unlike conventional segmentation-based association tracking methods, the proposed method uses the tracking results from the previous frame to segment the cell regions at the current frame. First, candidate cell regions are detected, and while there may be many false positives, there are very few false negatives. Next, optimized detection results are selected from the candidate regions and associated with the tracking results of the previous frame by resolving a linear programming problem. We quantitatively evaluated the proposed method using a variety of sequences. Results showed that our method has a better tracking performance than conventional segmentation-based association methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013 |
| Pages | 554-561 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - Sep 18 2013 |
| Externally published | Yes |
| Event | 10th IASTED International Conference on Biomedical Engineering, BioMed 2013 - Innsbruck, Austria Duration: Feb 13 2013 → Feb 15 2013 |
Publication series
| Name | Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013 |
|---|
Other
| Other | 10th IASTED International Conference on Biomedical Engineering, BioMed 2013 |
|---|---|
| Country | Austria |
| City | Innsbruck |
| Period | 2/13/13 → 2/15/13 |
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All Science Journal Classification (ASJC) codes
- Biomedical Engineering
Cite this
Cell tracking under high confluency conditions by candidate cell region detection-based association approach. / Bise, Ryoma; Maeda, Yoshitaka; Kim, Mee Hae; Kino-Oka, Masahiro.
Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013. 2013. p. 554-561 (Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Cell tracking under high confluency conditions by candidate cell region detection-based association approach
AU - Bise, Ryoma
AU - Maeda, Yoshitaka
AU - Kim, Mee Hae
AU - Kino-Oka, Masahiro
PY - 2013/9/18
Y1 - 2013/9/18
N2 - Automated tracking of cell population is an important element of research and discovery in the biology field. In this paper, we propose a method that tracks cells under highly confluent conditions by using the candidate cell region detection-based association approach. Unlike conventional segmentation-based association tracking methods, the proposed method uses the tracking results from the previous frame to segment the cell regions at the current frame. First, candidate cell regions are detected, and while there may be many false positives, there are very few false negatives. Next, optimized detection results are selected from the candidate regions and associated with the tracking results of the previous frame by resolving a linear programming problem. We quantitatively evaluated the proposed method using a variety of sequences. Results showed that our method has a better tracking performance than conventional segmentation-based association methods.
AB - Automated tracking of cell population is an important element of research and discovery in the biology field. In this paper, we propose a method that tracks cells under highly confluent conditions by using the candidate cell region detection-based association approach. Unlike conventional segmentation-based association tracking methods, the proposed method uses the tracking results from the previous frame to segment the cell regions at the current frame. First, candidate cell regions are detected, and while there may be many false positives, there are very few false negatives. Next, optimized detection results are selected from the candidate regions and associated with the tracking results of the previous frame by resolving a linear programming problem. We quantitatively evaluated the proposed method using a variety of sequences. Results showed that our method has a better tracking performance than conventional segmentation-based association methods.
UR - http://www.scopus.com/inward/record.url?scp=84883874225&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883874225&partnerID=8YFLogxK
U2 - 10.2316/P.2013.791-057
DO - 10.2316/P.2013.791-057
M3 - Conference contribution
AN - SCOPUS:84883874225
SN - 9780889869530
T3 - Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013
SP - 554
EP - 561
BT - Proceedings of the IASTED International Conference on Biomedical Engineering, BioMed 2013
ER -