| VIČAR, Tomáš, Jiri CHEMELIK, Roman JAKUBICEK, Larisa CHMELIKOVA, Jaromír GUMULEC, Jan BALVAN, Ivo PROVAZNÍK and Radim KOLAR. Self-supervised pretraining for transferable quantitative phase image cell segmentation. BIOMEDICAL OPTICS EXPRESS. WASHINGTON: OPTICAL SOC AMER, 2021, vol. 12, No 10, p. 6514-6528. ISSN 2156-7085. Available from: https://dx.doi.org/10.1364/BOE.433212. |
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@article{1826219,
author = {Vičar, Tomáš and Chemelik, Jiri and Jakubicek, Roman and Chmelikova, Larisa and Gumulec, Jaromír and Balvan, Jan and Provazník, Ivo and Kolar, Radim},
article_location = {WASHINGTON},
article_number = {10},
doi = {http://dx.doi.org/10.1364/BOE.433212},
keywords = {Self-supervised pretraining; transferable quantitative phase image cell segmentation},
language = {eng},
issn = {2156-7085},
journal = {BIOMEDICAL OPTICS EXPRESS},
title = {Self-supervised pretraining for transferable quantitative phase image cell segmentation},
url = {https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-12-10-6514&id=459853},
volume = {12},
year = {2021}
}
TY - JOUR
ID - 1826219
AU - Vičar, Tomáš - Chemelik, Jiri - Jakubicek, Roman - Chmelikova, Larisa - Gumulec, Jaromír - Balvan, Jan - Provazník, Ivo - Kolar, Radim
PY - 2021
TI - Self-supervised pretraining for transferable quantitative phase image cell segmentation
JF - BIOMEDICAL OPTICS EXPRESS
VL - 12
IS - 10
SP - 6514-6528
EP - 6514-6528
PB - OPTICAL SOC AMER
SN - 21567085
KW - Self-supervised pretraining
KW - transferable quantitative phase image cell segmentation
UR - https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-12-10-6514&id=459853
N2 - 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
ER -
VIČAR, Tomáš, Jiri CHEMELIK, Roman JAKUBICEK, Larisa CHMELIKOVA, Jaromír GUMULEC, Jan BALVAN, Ivo PROVAZNÍK and Radim KOLAR. Self-supervised pretraining for transferable quantitative phase image cell segmentation. \textit{BIOMEDICAL OPTICS EXPRESS}. WASHINGTON: OPTICAL SOC AMER, 2021, vol.~12, No~10, p.~6514-6528. ISSN~2156-7085. Available from: https://dx.doi.org/10.1364/BOE.433212.
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