Self-supervised pretraining for transferable quantitative phase image cell segmentation
dc.contributor.author | Vičar, Tomáš | cs |
dc.contributor.author | Chmelík, Jiří | cs |
dc.contributor.author | Jakubíček, Roman | cs |
dc.contributor.author | Chmelíková, Larisa | cs |
dc.contributor.author | Gumulec, Jaromír | cs |
dc.contributor.author | Balvan, Jan | cs |
dc.contributor.author | Provazník, Ivo | cs |
dc.contributor.author | Kolář, Radim | cs |
dc.coverage.issue | 10 | cs |
dc.coverage.volume | 12 | cs |
dc.date.accessioned | 2021-10-05T10:53:50Z | |
dc.date.available | 2021-10-05T10:53:50Z | |
dc.date.issued | 2021-09-24 | cs |
dc.description.abstract | In this paper, a novel U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is used in the post-processing step. Additionally, we proposed a self-supervised pretraining technique using nonlabelled data, which is trained to reconstruct multiple image distortions and improved the segmentation performance from 0.67 to 0.70 of object-wise intersection over union. Moreover, we publish a new dataset of manually labelled images suitable for this task together with the unlabelled data for self-supervised pretraining. | en |
dc.format | text | cs |
dc.format.extent | 6514-6528 | cs |
dc.format.mimetype | application/pdf | cs |
dc.identifier.citation | Biomedical Optics Express. 2021, vol. 12, issue 10, p. 6514-6528. | en |
dc.identifier.doi | 10.1364/BOE.433212 | cs |
dc.identifier.issn | 2156-7085 | cs |
dc.identifier.other | 172596 | cs |
dc.identifier.uri | http://hdl.handle.net/11012/201741 | |
dc.language.iso | en | cs |
dc.publisher | Optica Publishing Group | cs |
dc.relation.ispartof | Biomedical Optics Express | cs |
dc.relation.uri | https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-12-10-6514&id=459853 | cs |
dc.rights | (C) Optica Publishing Group | cs |
dc.rights.access | openAccess | cs |
dc.rights.sherpa | http://www.sherpa.ac.uk/romeo/issn/2156-7085/ | cs |
dc.subject | cell segmentation | en |
dc.subject | deep learning | en |
dc.subject | transfer learning | en |
dc.subject | self-supervised | en |
dc.title | Self-supervised pretraining for transferable quantitative phase image cell segmentation | en |
dc.type.driver | article | en |
dc.type.status | Peer-reviewed | en |
dc.type.version | publishedVersion | en |
sync.item.dbid | VAV-172596 | en |
sync.item.dbtype | VAV | en |
sync.item.insts | 2022.01.03 16:54:46 | en |
sync.item.modts | 2022.01.03 16:16:00 | en |
thesis.grantor | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrství | cs |
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