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dc.contributor.authorŠkrabánek, Pavelcs
dc.contributor.authorZahradníková, Alexandracs
dc.date.accessioned2020-08-04T11:02:54Z
dc.date.available2020-08-04T11:02:54Z
dc.date.issued2019-05-30cs
dc.identifier.citationPLOS ONE. 2019, vol. 14, issue 5, p. 1-18.en
dc.identifier.issn1932-6203cs
dc.identifier.other157176cs
dc.identifier.urihttp://hdl.handle.net/11012/179583
dc.description.abstractComputer assisted image acquisition techniques, including confocal microscopy, require efficient tools for an automatic sorting of vast amount of generated image data. The complexity of the classification process, absence of adequate tools, and insufficient amount of reference data has made the automated processing of images challenging. Mastering of this issue would allow implementation of statistical analysis in research areas such as in research on formation of t-tubules in cardiac myocytes. We developed a system aimed at automatic assessment of cardiomyocyte development stages (SAACS). The system classifies confocal images of cardiomyocytes with fluorescent dye stained sarcolemma. We based SAACS on a densely connected convolutional network (DenseNet) topology. We created a set of labelled source images, proposed an appropriate data augmentation technique and designed a class probability graph. We showed that the DenseNet topology, in combination with the augmentation technique is suitable for the given task, and that high-resolution images are instrumental for image categorization. SAACS, in combination with the automatic high-throughput confocal imaging, will allow application of statistical analysis in the research of the tubular system development or remodelling and loss.en
dc.formattextcs
dc.format.extent1-18cs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherPLOScs
dc.relation.ispartofPLOS ONEcs
dc.relation.urihttps://doi.org/10.1371/journal.pone.0216720cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectcardiomyocyte development stagesen
dc.subjectdensely connected convolutional networken
dc.subjectdeep learningen
dc.subjectclassification of object imagesen
dc.subjectconfocal microscopyen
dc.titleAutomatic assessment of the cardiomyocyte development stages from confocal microscopy images using deep convolutional networksen
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav automatizace a informatikycs
sync.item.dbidVAV-157176en
sync.item.dbtypeVAVen
sync.item.insts2020.08.04 13:02:54en
sync.item.modts2020.08.04 12:34:30en
dc.coverage.issue5cs
dc.coverage.volume14cs
dc.identifier.doi10.1371/journal.pone.0216720cs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1932-6203/cs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution 4.0 International
Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International