Self-supervised pretraining for transferable quantitative phase image cell segmentation.
Journal
Biomedical optics express
ISSN: 2156-7085
Titre abrégé: Biomed Opt Express
Pays: United States
ID NLM: 101540630
Informations de publication
Date de publication:
01 Oct 2021
01 Oct 2021
Historique:
received:
04
06
2021
revised:
03
08
2021
accepted:
24
08
2021
entrez:
8
11
2021
pubmed:
9
11
2021
medline:
9
11
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34745753
doi: 10.1364/BOE.433212
pii: 433212
pmc: PMC8547997
doi:
Types de publication
Journal Article
Langues
eng
Pagination
6514-6528Informations de copyright
© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest.
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