Publication details

Self-supervised pretraining for transferable quantitative phase image cell segmentation

Authors

VIČAR Tomáš CHEMELIK Jiri JAKUBICEK Roman CHMELIKOVA Larisa GUMULEC Jaromír BALVAN Jan PROVAZNÍK Ivo KOLAR Radim

Year of publication 2021
Type Article in Periodical
Magazine / Source BIOMEDICAL OPTICS EXPRESS
MU Faculty or unit

Faculty of Medicine

Citation
Web https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-12-10-6514&id=459853
Doi http://dx.doi.org/10.1364/BOE.433212
Keywords Self-supervised pretraining; transferable quantitative phase image cell segmentation
Description 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. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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