Self-supervised pretraining for transferable quantitative phase
image cell segmentation

J 2021

Self-supervised pretraining for transferable quantitative phase image cell segmentation

VIČAR, Tomáš, Jiri CHEMELIK, Roman JAKUBICEK, Larisa CHMELIKOVA, Jaromír GUMULEC et. al.

Basic information

Original name

Self-supervised pretraining for transferable quantitative phase image cell segmentation

Authors

VIČAR, Tomáš (203 Czech Republic, belonging to the institution), Jiri CHEMELIK (203 Czech Republic), Roman JAKUBICEK (203 Czech Republic), Larisa CHMELIKOVA (203 Czech Republic), Jaromír GUMULEC (203 Czech Republic, belonging to the institution), Jan BALVAN (203 Czech Republic, belonging to the institution), Ivo PROVAZNÍK (203 Czech Republic) and Radim KOLAR (203 Czech Republic, guarantor)

Edition

BIOMEDICAL OPTICS EXPRESS, WASHINGTON, OPTICAL SOC AMER, 2021, 2156-7085

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30224 Radiology, nuclear medicine and medical imaging

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.562

RIV identification code

RIV/00216224:14110/21:00123919

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1364/BOE.433212

UT WoS

000703871700005

Keywords in English

Self-supervised pretraining; transferable quantitative phase image cell segmentation

Abstract

V originále

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

Links

LM2018140, research and development project

Name: e-Infrastruktura CZ (Acronym: e-INFRA CZ)

Investor: Ministry of Education, Youth and Sports of the CR

Citovat

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.

@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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