Issue 9, 2024

HFM-Tracker: a cell tracking algorithm based on hybrid feature matching

Abstract

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.

Graphical abstract: HFM-Tracker: a cell tracking algorithm based on hybrid feature matching

Supplementary files

Article information

Article type
Paper
Submitted
05 2 2024
Accepted
14 3 2024
First published
19 3 2024

Analyst, 2024,149, 2629-2636

HFM-Tracker: a cell tracking algorithm based on hybrid feature matching

Y. Zhao, K. Chen, X. Shen, M. Li, Y. Wan, C. Yang, R. Yu, Y. Long, F. Yan and Y. Ying, Analyst, 2024, 149, 2629 DOI: 10.1039/D4AN00199K

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