Cargando…

Color restoration based on digital pathology image

OBJECTIVE: Protective color restoration of faded digital pathology images based on color transfer algorithm. METHODS: Twenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irr...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Guoxin, Yan, Xiong, Wang, Huizhe, Li, Fei, Yang, Rui, Xu, Jing, Liu, Xin, Li, Xiaomao, Zou, Xiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10306179/
https://www.ncbi.nlm.nih.gov/pubmed/37379301
http://dx.doi.org/10.1371/journal.pone.0287704
_version_ 1785065882208174080
author Sun, Guoxin
Yan, Xiong
Wang, Huizhe
Li, Fei
Yang, Rui
Xu, Jing
Liu, Xin
Li, Xiaomao
Zou, Xiao
author_facet Sun, Guoxin
Yan, Xiong
Wang, Huizhe
Li, Fei
Yang, Rui
Xu, Jing
Liu, Xin
Li, Xiaomao
Zou, Xiao
author_sort Sun, Guoxin
collection PubMed
description OBJECTIVE: Protective color restoration of faded digital pathology images based on color transfer algorithm. METHODS: Twenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irradiated with sunlight to simulate natural fading, and every 7 days was a fading cycle, and a total of 8 cycles were experienced. At the end of each cycle, the sections were digitally scanned to retain clear images, and the color changes of the sections during the fading process were recorded. The color transfer algorithm was applied to restore the color of the faded images; Adobe Lightroom Classic software presented the histogram of the image color distribution; UNet++ cell recognition segmentation model was used to identify the color restored images; Natural Image Quality Evaluator (NIQE), Information Entropy (Entropy), and Average Gradient (AG) were applied to evaluate the quality of the restored images. RESULTS: The restored image color met the diagnostic needs of pathologists. Compared with the faded images, the NIQE value decreased (P<0.05), Entropy value increased (P<0.01), and AG value increased (P<0.01). The cell recognition rate of the restored image was significantly improved. CONCLUSION: The color transfer algorithm can effectively repair faded pathology images, restore the color contrast between nucleus and cytoplasm, improve the image quality, meet the diagnostic needs and improve the cell recognition rate of the deep learning model.
format Online
Article
Text
id pubmed-10306179
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-103061792023-06-29 Color restoration based on digital pathology image Sun, Guoxin Yan, Xiong Wang, Huizhe Li, Fei Yang, Rui Xu, Jing Liu, Xin Li, Xiaomao Zou, Xiao PLoS One Research Article OBJECTIVE: Protective color restoration of faded digital pathology images based on color transfer algorithm. METHODS: Twenty fresh tissue samples of invasive breast cancer from the pathology department of Qingdao Central Hospital in 2021 were screened. After HE staining, HE stained sections were irradiated with sunlight to simulate natural fading, and every 7 days was a fading cycle, and a total of 8 cycles were experienced. At the end of each cycle, the sections were digitally scanned to retain clear images, and the color changes of the sections during the fading process were recorded. The color transfer algorithm was applied to restore the color of the faded images; Adobe Lightroom Classic software presented the histogram of the image color distribution; UNet++ cell recognition segmentation model was used to identify the color restored images; Natural Image Quality Evaluator (NIQE), Information Entropy (Entropy), and Average Gradient (AG) were applied to evaluate the quality of the restored images. RESULTS: The restored image color met the diagnostic needs of pathologists. Compared with the faded images, the NIQE value decreased (P<0.05), Entropy value increased (P<0.01), and AG value increased (P<0.01). The cell recognition rate of the restored image was significantly improved. CONCLUSION: The color transfer algorithm can effectively repair faded pathology images, restore the color contrast between nucleus and cytoplasm, improve the image quality, meet the diagnostic needs and improve the cell recognition rate of the deep learning model. Public Library of Science 2023-06-28 /pmc/articles/PMC10306179/ /pubmed/37379301 http://dx.doi.org/10.1371/journal.pone.0287704 Text en © 2023 Sun et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sun, Guoxin
Yan, Xiong
Wang, Huizhe
Li, Fei
Yang, Rui
Xu, Jing
Liu, Xin
Li, Xiaomao
Zou, Xiao
Color restoration based on digital pathology image
title Color restoration based on digital pathology image
title_full Color restoration based on digital pathology image
title_fullStr Color restoration based on digital pathology image
title_full_unstemmed Color restoration based on digital pathology image
title_short Color restoration based on digital pathology image
title_sort color restoration based on digital pathology image
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10306179/
https://www.ncbi.nlm.nih.gov/pubmed/37379301
http://dx.doi.org/10.1371/journal.pone.0287704
work_keys_str_mv AT sunguoxin colorrestorationbasedondigitalpathologyimage
AT yanxiong colorrestorationbasedondigitalpathologyimage
AT wanghuizhe colorrestorationbasedondigitalpathologyimage
AT lifei colorrestorationbasedondigitalpathologyimage
AT yangrui colorrestorationbasedondigitalpathologyimage
AT xujing colorrestorationbasedondigitalpathologyimage
AT liuxin colorrestorationbasedondigitalpathologyimage
AT lixiaomao colorrestorationbasedondigitalpathologyimage
AT zouxiao colorrestorationbasedondigitalpathologyimage