{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:43Z","timestamp":1772138023160,"version":"3.50.1"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,5,5]],"date-time":"2024-05-05T00:00:00Z","timestamp":1714867200000},"content-version":"vor","delay-in-days":39,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang University and the Fundamental Research Funds for the Central Universities","award":["2019QN30001"],"award-info":[{"award-number":["2019QN30001"]}]},{"name":"Zhejiang University-University of Edinburgh"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Computational analysis of fluorescent timelapse microscopy images at the single-cell level is a powerful approach to study cellular changes that dictate important cell fate decisions. Core to this approach is the need to generate reliable cell segmentations and classifications necessary for accurate quantitative analysis. Deep learning\u2013based convolutional neural networks (CNNs) have emerged as a promising solution to these challenges. However, current CNNs are prone to produce noisy cell segmentations and classifications, which is a significant barrier to constructing accurate single-cell lineages. To address this, we developed a novel algorithm called Single Cell Track (SC-Track), which employs a hierarchical probabilistic cache cascade model based on biological observations of cell division and movement dynamics. Our results show that SC-Track performs better than a panel of publicly available cell trackers on a diverse set of cell segmentation types. This cell-tracking performance was achieved without any parameter adjustments, making SC-Track an excellent generalized algorithm that can maintain robust cell-tracking performance in varying cell segmentation qualities, cell morphological appearances and imaging conditions. Furthermore, SC-Track is equipped with a cell class correction function to improve the accuracy of cell classifications in multiclass cell segmentation time series. These features together make SC-Track a robust cell-tracking algorithm that works well with noisy cell instance segmentation and classification predictions from CNNs to generate accurate single-cell lineages and classifications.<\/jats:p>","DOI":"10.1093\/bib\/bbae192","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T02:22:27Z","timestamp":1712715747000},"source":"Crossref","is-referenced-by-count":2,"title":["SC-Track: a robust cell-tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations"],"prefix":"10.1093","volume":"25","author":[{"given":"Chengxin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Cardiovascular Medicine , The Second Affiliated Hospital, , Hangzhou, 310058 , P. R. China"},{"name":"Zhejiang University School of Medicine , The Second Affiliated Hospital, , Hangzhou, 310058 , P. R. China"},{"name":"Centre for Cellular Biology and Signalling, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University , Haining, 314400 , P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang Shuang","family":"Xie","sequence":"additional","affiliation":[{"name":"Centre for Cellular Biology and Signalling, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University , Haining, 314400 , P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Centre for Cellular Biology and Signalling, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University , Haining, 314400 , P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Septavera","family":"Sharvia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Hull , Hull, HU6 7RX , UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5974-1650","authenticated-orcid":false,"given":"Kuan Yoow","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine , The Second Affiliated Hospital, , Hangzhou, 310058 , P. R. China"},{"name":"Zhejiang University School of Medicine , The Second Affiliated Hospital, , Hangzhou, 310058 , P. R. China"},{"name":"Centre for Cellular Biology and Signalling, Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University , Haining, 314400 , P. R. 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