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; Jiri CHEMELIK; Roman JAKUBICEK; Larisa CHMELIKOVA; Jaromír GUMULEC; Jan BALVAN; Ivo PROVAZNÍK a Radim KOLAR

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

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14110/21:00123919

Organizační jednotka

Lékařská fakulta

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://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 = {https://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://doi.org/10.1364/BOE.433212.

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