Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: A case study on HeLa line

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12520%2F22%3A43905733" target="_blank" >RIV/60076658:12520/22:43905733 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.compbiomed.2022.105805" target="_blank" >https://doi.org/10.1016/j.compbiomed.2022.105805</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compbiomed.2022.105805" target="_blank" >10.1016/j.compbiomed.2022.105805</a>

Result language

angličtina

Original language name

Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: A case study on HeLa line

Original language description

Living cell segmentation from bright-field light microscopy images is challenging due to the image complexity and temporal changes in the living cells. Recently developed deep learning (DL)-based methods became popular in medical and microscopy image segmentation tasks due to their success and promising outcomes. The main objective of this paper is to develop a deep learning, U-Net-based method to segment the living cells of the HeLa line in bright-field transmitted light microscopy. To find the most suitable architecture for our datasets, a residual attention U-Net was proposed and compared with an attention and a simple U-Net architecture. The attention mechanism highlights the remarkable features and suppresses activations in the irrelevant image regions. The residual mechanism overcomes with vanishing gradient problem. The Mean-IoU score for our datasets reaches 0.9505, 0.9524, and 0.9530 for the simple, attention, and residual attention U-Net, respectively. The most accurate semantic segmentation results was achieved in the Mean-IoU and Dice metrics by applying the residual and attention mechanisms together. The watershed method applied to this best – Residual Attention – semantic segmentation result gave the segmentation with the specific information for each cell. © 2022 Elsevier Ltd

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LM2018099" target="_blank" >LM2018099: South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computers in Biology and Medicine

ISSN

0010-4825

e-ISSN

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Volume of the periodical

147

Issue of the periodical within the volume

neuvedeno

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

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UT code for WoS article

000944802100001

EID of the result in the Scopus database

2-s2.0-85133546528