Open AccessSelf-supervised pretraining for transferable quantitative phase image cell segmentation
Author(s) -
Pavel Kopel,
Jiří Chmelík,
Roman Jakubíček,
Larisa Chmelikova,
Jaromír Gumulec,
Jan Balvan,
Ivo Provazník,
Radim Kolář
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.433212
Subject(s) - computer science , artificial intelligence , segmentation , transfer of learning , pattern recognition (psychology) , image segmentation , image processing , intersection (aeronautics) , computer vision , task (project management) , image (mathematics) , management , economics , engineering , aerospace engineering
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.