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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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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 |
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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 |
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