Automated classification of cell morphology by coherence-controlled holographic microscopy

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dc.contributor.authorŠtrbková, Lenkacs
dc.contributor.authorZicha, Danielcs
dc.contributor.authorVeselý, Pavelcs
dc.contributor.authorChmelík, Radimcs
dc.coverage.issue8cs
dc.coverage.volume22cs
dc.date.accessioned2020-08-04T11:02:59Z
dc.date.available2020-08-04T11:02:59Z
dc.date.issued2017-08-23cs
dc.description.abstractIn the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherencecontrolled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.en
dc.formattextcs
dc.format.extent086008-1-086008-9cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationJOURNAL OF BIOMEDICAL OPTICS. 2017, vol. 22, issue 8, p. 086008-1-086008-9.en
dc.identifier.doi10.1117/1.JBO.22.8.086008cs
dc.identifier.issn1083-3668cs
dc.identifier.other138402cs
dc.identifier.urihttp://hdl.handle.net/11012/84156
dc.language.isoencs
dc.publisherSPIEcs
dc.relation.ispartofJOURNAL OF BIOMEDICAL OPTICScs
dc.relation.urihttp://dx.doi.org/10.1117/1.JBO.22.8.086008cs
dc.rightsCreative Commons Attribution 3.0 Unportedcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1083-3668/cs
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/cs
dc.subjectcoherence-controlled holographic microscopyen
dc.subjectdigital holographic microscopyen
dc.subjectquantitative phase imagingen
dc.subjectsupervised machine learningen
dc.subjectclassificationen
dc.subjectcell morphologyen
dc.titleAutomated classification of cell morphology by coherence-controlled holographic microscopyen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-138402en
sync.item.dbtypeVAVen
sync.item.insts2020.08.04 13:02:59en
sync.item.modts2020.08.04 12:47:21en
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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