{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T05:08:18Z","timestamp":1768194498758,"version":"3.49.0"},"reference-count":5,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2020YFA0906900"],"award-info":[{"award-number":["2020YFA0906900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61773230"],"award-info":[{"award-number":["61773230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61721003"],"award-info":[{"award-number":["61721003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>Recent advances of long-term time-lapse microscopy have made it easy for researchers to quantify cell behavior and molecular dynamics at single-cell resolution. However, the lack of easy-to-use software tools optimized for customized research is still a major challenge for quantitatively understanding biological processes through microscopy images. Here, we present CellTracker, a highly integrated graphical user interface software, for automated cell segmentation and tracking of time-lapse microscopy images. It covers essential steps in image analysis including project management, image pre-processing, cell segmentation, cell tracking, manually correction and statistical analysis such as the quantification of cell size and fluorescence intensity, etc. Furthermore, CellTracker provides an annotation tool and supports model training from scratch, thus proposing a flexible and scalable solution for customized dataset analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>CellTracker is an open-source software under the GPL-3.0 license. It is implemented in Python and provides an easy-to-use graphical user interface. The source code, instruction manual and demos can be found at https:\/\/github.com\/WangLabTHU\/CellTracker.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa1106","type":"journal-article","created":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T20:13:07Z","timestamp":1609272787000},"page":"285-287","source":"Crossref","is-referenced-by-count":17,"title":["CellTracker: an automated toolbox for single-cell segmentation and tracking of time-lapse microscopy images"],"prefix":"10.1093","volume":"37","author":[{"given":"Tao","family":"Hu","sequence":"first","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University , Beijing 100084, China"}]},{"given":"Shixiong","family":"Xu","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University , Beijing 100084, China"}]},{"given":"Lei","family":"Wei","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University , Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9684-5643","authenticated-orcid":false,"given":"Xuegong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University , Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2965-8036","authenticated-orcid":false,"given":"Xiaowo","family":"Wang","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and System Biology, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University , Beijing 100084, China"}]}],"member":"286","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"2023051511000304900_btaa1106-B1","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1007\/s40484-018-0156-3","article-title":"Characterizing robustness and sensitivity of convolutional neural networks for quantitative analysis of mitochondrial morphology","volume":"6","author":"Chai","year":"2018","journal-title":"Q. 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