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Cell tracking with multifeature fusion.
- Source :
- Journal of Supercomputing; Nov2023, Vol. 79 Issue 17, p20001-20018, 18p
- Publication Year :
- 2023
-
Abstract
- Cell tracking is currently a powerful tool in a variety of biomedical research topics. Most cell tracking algorithms follow the tracking by detection paradigm. Detection is critical for subsequent tracking. Unfortunately, very accurate detection is not easy due to many factors like densely populated, low contrast, and possible impurities included. Keeping tracking multiple cells across frames suffers many difficulties, as cells may have similar appearance, they may change their shapes, and nearby cells may interact each other. In this paper, we propose a unified tracking-by-detection framework, where a powerful detector AttentionUnet++, a multimodal extension of the Efficient Convolution Operators algorithm, and an effective data association algorithm are included. Experiments show that the proposed algorithm can outperform many existing cell tracking algorithms. [ABSTRACT FROM AUTHOR]
- Subjects :
- TRACKING algorithms
MEDICAL research
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 09208542
- Volume :
- 79
- Issue :
- 17
- Database :
- Complementary Index
- Journal :
- Journal of Supercomputing
- Publication Type :
- Academic Journal
- Accession number :
- 172445257
- Full Text :
- https://doi.org/10.1007/s11227-023-05384-z