Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition

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dc.contributor.authorŠtrbková, Lenkacs
dc.contributor.authorCarson, Brittany B.cs
dc.contributor.authorVincent, Theresacs
dc.contributor.authorVeselý, Pavelcs
dc.contributor.authorChmelík, Radimcs
dc.coverage.issue8cs
dc.coverage.volume25cs
dc.date.accessioned2021-04-22T10:54:15Z
dc.date.available2021-04-22T10:54:15Z
dc.date.issued2020-08-31cs
dc.description.abstractSignificance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification. Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes. Approach: The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images. Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes. Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.en
dc.formattextcs
dc.format.extent1-18cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJOURNAL OF BIOMEDICAL OPTICS. 2020, vol. 25, issue 8, p. 1-18.en
dc.identifier.doi10.1117/1.JBO.25.8.086502cs
dc.identifier.issn1560-2281cs
dc.identifier.other165957cs
dc.identifier.urihttp://hdl.handle.net/11012/196571
dc.language.isoencs
dc.publisherSPIEcs
dc.relation.ispartofJOURNAL OF BIOMEDICAL OPTICScs
dc.relation.urihttps://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-25/issue-08/086502/Automated-interpretation-of-time-lapse-quantitative-phase-image-by-machine/10.1117/1.JBO.25.8.086502.full?SSO=1cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1560-2281/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectdigital holographic microscopyen
dc.subjectquantitative phase imagingen
dc.subjectsupervised machine learningen
dc.subjectepithelial-mesenchymal transitionen
dc.titleAutomated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transitionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-165957en
sync.item.dbtypeVAVen
sync.item.insts2021.04.22 12:54:15en
sync.item.modts2021.04.22 12:14:35en
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Experimentální biofotonikacs
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav fyzikálního inženýrstvícs
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