| Autor: |
Zhao Y; School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, 200237 Shanghai, P. R. China. wanyongjing@ecust.edu.cn., Chen KL; School of Chemistry and Chemical Engineering, Molecular Sensing and Imaging Center (MSIC), Nanjing University, Nanjing 210023, P. R. China. yurujia@nju.edu.cn., Shen XY; School of Electronic Sciences and Engineering, Nanjing University, Nanjing, 210023, China., Li MK; School of Chemistry and Chemical Engineering, Molecular Sensing and Imaging Center (MSIC), Nanjing University, Nanjing 210023, P. R. China. yurujia@nju.edu.cn., Wan YJ; School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, 200237 Shanghai, P. R. China. wanyongjing@ecust.edu.cn., Yang C; School of Electronic Sciences and Engineering, Nanjing University, Nanjing, 210023, China., Yu RJ; School of Chemistry and Chemical Engineering, Molecular Sensing and Imaging Center (MSIC), Nanjing University, Nanjing 210023, P. R. China. yurujia@nju.edu.cn., Long YT; School of Chemistry and Chemical Engineering, Molecular Sensing and Imaging Center (MSIC), Nanjing University, Nanjing 210023, P. R. China. yurujia@nju.edu.cn., Yan F; School of Electronic Sciences and Engineering, Nanjing University, Nanjing, 210023, China., Ying YL; School of Information Science and Engineering, East China University of Science and Technology, 130 Meilong Road, 200237 Shanghai, P. R. China. wanyongjing@ecust.edu.cn.; Chemistry and Biomedicine Innovation Center, Nanjing University, Nanjing 210023, P. R. China. |
| Abstrakt: |
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. |
