Label-free live cell recognition and tracking for biological discoveries and translational applications

被引：0

作者：

Biqi Chen ^{[1
]}

Zi Yin ^{[2
]}

Billy Wai-Lung Ng ^{[3
]}

Dan Michelle Wang ^{[4
]}

Rocky S. Tuan ^{[5
]}

Ryoma Bise ^{[1
]}

Dai Fei Elmer Ker ^{[6
]}

机构：

[1] The Chinese University of Hong Kong,Institute for Tissue Engineering and Regenerative Medicine

[2] Zhejiang University,Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, School of Medicine

[3] Zhejiang University,Institute of Sports Medicine

[4] The Chinese University of Hong Kong,School of Pharmacy, Faculty of Medicine

[5] The Chinese University of Hong Kong,Li Ka Shing Institute of Health Sciences, Faculty of Medicine

[6] The Chinese University of Hong Kong,School of Biomedical Sciences, Faculty of Medicine

[7] The Chinese University of Hong Kong,Ministry of Education Key Laboratory for Regenerative Medicine, School of Biomedical Sciences, Faculty of Medicine

[8] The Chinese University of Hong Kong,Department of Orthopaedics and Traumatology, Faculty of Medicine

[9] Hong Kong Science Park,Center for Neuromusculoskeletal Restorative Medicine

[10] Kyushu University,Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering

[11] The Hong Kong Polytechnic University,Department of Biomedical Engineering, Faculty of Engineering

来源：

npj Imaging | / 2卷 / 1期

关键词：

D O I：

10.1038/s44303-024-00046-y

中图分类号：

学科分类号：

摘要：

Label-free, live cell recognition (i.e. instance segmentation) and tracking using computer vision-aided recognition can be a powerful tool that rapidly generates multi-modal readouts of cell populations at single cell resolution. However, this technology remains hindered by the lack of accurate, universal algorithms. This review presents related biological and computer vision concepts to bridge these disciplines, paving the way for broad applications in cell-based diagnostics, drug discovery, and biomanufacturing.

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