Self-supervised pretraining for transferable quantitative phase image cell segmentation.
Biomed Opt Express
; 12(10): 6514-6528, 2021 Oct 01.
Article
em En
| MEDLINE
| ID: mdl-34745753
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.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Biomed Opt Express
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
República Tcheca