Cell And Sub-Cellular Segmentation In Quantitative Phase Imaging Using U-Net

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but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.contributor.authorMajerčík, Jakub
dc.contributor.authorŠpaček, Michal
dc.date.accessioned2023-01-06T10:05:43Z
dc.date.available2023-01-06T10:05:43Z
dc.date.issued2021cs
dc.description.abstractThe ability to automatically segment images, especially microscopy images of cells, opensnew opportunities in cancer research or other practical applications. Recent advancements in deeplearning enabled for effective single-cell segmentation, however, automatic segmentation of subcellularregions is still challenging. This work describes an implementation of a U-net neural networkfor label-free segmentation of sub-cellular regions on images of adherent prostate cancer cells,specifically PC-3 and 22Rv1. Using the best performing approach, out of all that have been tested,we have managed to distinguish between objects and background with average dice coefficients of0.83, 0.78 and 0.63 for whole cells, nuclei and nucleoli respectivelyen
dc.formattextcs
dc.format.extent9-12cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 9-12. ISBN 978-80-214-5943-4cs
dc.identifier.doi10.13164/eeict.2021.9
dc.identifier.isbn978-80-214-5943-4
dc.identifier.urihttp://hdl.handle.net/11012/200823
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 27st Conference STUDENT EEICT 2021: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectcell segmentationen
dc.subjectdeep learningen
dc.subjectneural networken
dc.subjectquantitative phase imagingen
dc.subjectnucleus,nucleolusen
dc.titleCell And Sub-Cellular Segmentation In Quantitative Phase Imaging Using U-Neten
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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