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.

Základní údaje

Originální název

Self-supervised pretraining for transferable quantitative phase image cell segmentation

Autoři

VIČAR, Tomáš (203 Česká republika, domácí), Jiri CHEMELIK (203 Česká republika), Roman JAKUBICEK (203 Česká republika), Larisa CHMELIKOVA (203 Česká republika), Jaromír GUMULEC (203 Česká republika, domácí), Jan BALVAN (203 Česká republika, domácí), Ivo PROVAZNÍK (203 Česká republika) a Radim KOLAR (203 Česká republika, garant)

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30224 Radiology, nuclear medicine and medical imaging

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.562

Kód RIV

RIV/00216224:14110/21:00123919

Organizační jednotka

Lékařská fakulta

DOI

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

UT WoS

000703871700005

Klíčová slova anglicky

Self-supervised pretraining; transferable quantitative phase image cell segmentation

Štítky

14110518, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 1. 2022 08:29, Mgr. Tereza Miškechová

Anotace

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

Návaznosti

LM2018140, projekt VaV

Název: e-Infrastruktura CZ (Akronym: e-INFRA CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

Citovat

VIČAR, Tomáš, Jiri CHEMELIK, Roman JAKUBICEK, Larisa CHMELIKOVA, Jaromír GUMULEC, Jan BALVAN, Ivo PROVAZNÍK a Radim KOLAR. Self-supervised pretraining for transferable quantitative phase image cell segmentation. BIOMEDICAL OPTICS EXPRESS. WASHINGTON: OPTICAL SOC AMER, 2021, roč. 12, č. 10, s. 6514-6528. ISSN 2156-7085. Dostupné z: 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 a Radim KOLAR. Self-supervised pretraining for transferable quantitative phase image cell segmentation. \textit{BIOMEDICAL OPTICS EXPRESS}. WASHINGTON: OPTICAL SOC AMER, 2021, roč.~12, č.~10, s.~6514-6528. ISSN~2156-7085. Dostupné z: https://dx.doi.org/10.1364/BOE.433212.

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