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HFM-Tracker: a cell tracking algorithm based on hybrid feature matching
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DOI：10.1039/d4an00199k.png)
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Cell migration is known to be a fundamental biological process, playing an essential role in development, homeostasis, and diseases. This paper introduces a cell tracking algorithm named HFM-Tracker (Hybrid Feature Matching Tracker) that automatically identifies cell migration behaviours in consecutive images. It combines Contour Attention (CA) and Adaptive Confusion Matrix (ACM) modules to accurately capture cell contours in each image and track the dynamic behaviors of migrating cells in the field of view. Cells are firstly located and identified via the CA module-based cell detection network, and then associated and tracked via a cell tracking algorithm employing a hybrid feature-matching strategy. This proposed HFM-Tracker exhibits superiorities in cell detection and tracking, achieving 75% in MOTA (Multiple Object Tracking Accuracy) and 65% in IDF1 (ID F1 score). It provides quantitative analysis of the cell morphology and migration features, which could further help in understanding the complicated and diverse cell migration processes. A novel cell tracking algorithm, named HFM-Tracker (Hybrid Feature Matching Tracker), is proposed to accurately track the migratory behavior of cells through the capture of time-lapse cell images.
Keyword:
MIGRATION
METASTASIS
期刊
A
IF:
3.3
论文数:
1.6W
被引数:
3.3W
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引用论文
Segmenting and tracking cell instances with cosine embeddings and recurrent hourglass networks.png)
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