Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/113663
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dc.contributorDepartment of Biomedical Engineeringen_US
dc.creatorChen, Ben_US
dc.creatorYin, Zen_US
dc.creatorNg, BWLen_US
dc.creatorWang, DMen_US
dc.creatorTuan, RSen_US
dc.creatorBise, Ren_US
dc.creatorKer, DFEen_US
dc.date.accessioned2025-06-17T01:34:02Z-
dc.date.available2025-06-17T01:34:02Z-
dc.identifier.urihttp://hdl.handle.net/10397/113663-
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.rights© The Author(s) 2024en_US
dc.rightsThis article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.en_US
dc.rightsThe following publication Chen, B., Yin, Z., Ng, B.WL. et al. Label-free live cell recognition and tracking for biological discoveries and translational applications. npj Imaging 2, 41 (2024) is available at https://doi.org/10.1038/s44303-024-00046-y.en_US
dc.titleLabel-free live cell recognition and tracking for biological discoveries and translational applicationsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume2en_US
dc.identifier.doi10.1038/s44303-024-00046-yen_US
dcterms.abstractLabel-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.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational Journal of Contemporary Hospitality Management, 7 Oct. 2024, v. 2, 41en_US
dcterms.isPartOfnpj imagingen_US
dcterms.issued2024-10-07-
dc.identifier.eissn2948-197Xen_US
dc.identifier.artn41en_US
dc.description.validate202506 bcwhen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera3712-
dc.identifier.SubFormID50820-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work was supported by Hong Kong Health Bureau (D.F.E.K.: Health Medical and Research Fund, 08190466; D.M.W.: Health Medical and Research Fund, 07180686), Hong Kong Innovation and Technology Commission (D.F.E.K.: Tier 3 Award, ITS/090/18; D.M.W.: ITS/ 333/18; D.F.E.K., D.M.W., and R.S.T.: Health@InnoHK programme), Hong Kong Research Grants Council (D.F.E.K.: Early Career Scheme Award, 24201720; General Research Fund: 14213922; D.M.W.: General Research Fund: 14118620 and 14121121), National Natural Science Foundation of China /Research Grants Council Joint Research Scheme (D.M.W.: N_CUHK409/23), and The Hong Kong Polytechnic University (Startup Grant, DFEK). The support of the Lee Quo Wei and Lee Yick Hoi Lun Professorship in Tissue Engineering and Regenerative Medicine (R.S.T.) is also gratefully acknowledged.en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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