Self-supervised pretraining for transferable quantitative phase image cell segmentation.
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ABSTRACT:
SUBMITTER: Vicar T
PROVIDER: S-EPMC8547997 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Self-supervised pretraining for transferable quantitative phase image cell segmentation.
Vicar Tomas T Chmelik Jiri J Jakubicek Roman R Chmelikova Larisa L Gumulec Jaromir J Balvan Jan J Provaznik Ivo I Kolar Radim R
Biomedical optics express 20210924 10
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 d ...[more]
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