Publication detail

Self-supervised pretraining for transferable quantitative phase image cell segmentation

VIČAR, T. CHMELÍK, J. JAKUBÍČEK, R. CHMELÍKOVÁ, L. GUMULEC, J. BALVAN, J. PROVAZNÍK, I. KOLÁŘ, R.

Original Title

Self-supervised pretraining for transferable quantitative phase image cell segmentation

Type

journal article in Web of Science

Language

English

Original Abstract

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.

Keywords

cell segmentation, deep learning, transfer learning, self-supervised

Authors

VIČAR, T.; CHMELÍK, J.; JAKUBÍČEK, R.; CHMELÍKOVÁ, L.; GUMULEC, J.; BALVAN, J.; PROVAZNÍK, I.; KOLÁŘ, R.

Released

24. 9. 2021

Publisher

Optica Publishing Group

Location

2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036

ISBN

2156-7085

Periodical

Biomedical Optics Express

Year of study

12

Number

10

State

United States of America

Pages from

6514

Pages to

6528

Pages count

15

URL

Full text in the Digital Library

BibTex

@article{BUT172596,
  author="Tomáš {Vičar} and Jiří {Chmelík} and Roman {Jakubíček} and Larisa {Chmelíková} and Jaromír {Gumulec} and Jan {Balvan} and Valentine {Provazník} and Radim {Kolář}",
  title="Self-supervised pretraining for transferable quantitative phase image cell segmentation",
  journal="Biomedical Optics Express",
  year="2021",
  volume="12",
  number="10",
  pages="6514--6528",
  doi="10.1364/BOE.433212",
  issn="2156-7085",
  url="https://www.osapublishing.org/boe/fulltext.cfm?uri=boe-12-10-6514&id=459853"
}