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Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN

Colorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measur...

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Autores principales: Meng, Jie, Xue, Linyan, Chang, Ying, Zhang, Jianguang, Chang, Shilong, Liu, Kun, Liu, Shuang, Wang, Bangmao, Yang, Kun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968546/
https://www.ncbi.nlm.nih.gov/pubmed/33817247
http://dx.doi.org/10.1515/biol-2020-0055
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author Meng, Jie
Xue, Linyan
Chang, Ying
Zhang, Jianguang
Chang, Shilong
Liu, Kun
Liu, Shuang
Wang, Bangmao
Yang, Kun
author_facet Meng, Jie
Xue, Linyan
Chang, Ying
Zhang, Jianguang
Chang, Shilong
Liu, Kun
Liu, Shuang
Wang, Bangmao
Yang, Kun
author_sort Meng, Jie
collection PubMed
description Colorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measure of the ability of a colonoscopist to identify and remove precancerous colorectal polyps, varies significantly among endoscopists. Here, we attempt to use a convolutional neural network (CNN) to generate a unique computer-aided diagnosis (CAD) system by exploring in detail the multiple-scale performance of deep neural networks. We applied this system to 3,375 hand-labeled images from the screening colonoscopies of 1,197 patients; of whom, 3,045 were assigned to the training dataset and 330 to the testing dataset. The images were diagnosed simply as either an adenomatous or non-adenomatous polyp. When applied to the testing dataset, our CNN-CAD system achieved a mean average precision of 89.5%. We conclude that the proposed framework could increase the ADR and decrease the incidence of interval CRCs, although further validation through large multicenter trials is required.
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spelling pubmed-79685462021-04-01 Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN Meng, Jie Xue, Linyan Chang, Ying Zhang, Jianguang Chang, Shilong Liu, Kun Liu, Shuang Wang, Bangmao Yang, Kun Open Life Sci Topical Issue on Computing and Artificial Techniques for Life Science Applications Colorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measure of the ability of a colonoscopist to identify and remove precancerous colorectal polyps, varies significantly among endoscopists. Here, we attempt to use a convolutional neural network (CNN) to generate a unique computer-aided diagnosis (CAD) system by exploring in detail the multiple-scale performance of deep neural networks. We applied this system to 3,375 hand-labeled images from the screening colonoscopies of 1,197 patients; of whom, 3,045 were assigned to the training dataset and 330 to the testing dataset. The images were diagnosed simply as either an adenomatous or non-adenomatous polyp. When applied to the testing dataset, our CNN-CAD system achieved a mean average precision of 89.5%. We conclude that the proposed framework could increase the ADR and decrease the incidence of interval CRCs, although further validation through large multicenter trials is required. De Gruyter 2020-08-14 /pmc/articles/PMC7968546/ /pubmed/33817247 http://dx.doi.org/10.1515/biol-2020-0055 Text en © 2020 Jie Meng et al., published by De Gruyter http://creativecommons.org/licenses/by/4.0 This work is licensed under the Creative Commons Attribution 4.0 International License.
spellingShingle Topical Issue on Computing and Artificial Techniques for Life Science Applications
Meng, Jie
Xue, Linyan
Chang, Ying
Zhang, Jianguang
Chang, Shilong
Liu, Kun
Liu, Shuang
Wang, Bangmao
Yang, Kun
Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN
title Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN
title_full Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN
title_fullStr Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN
title_full_unstemmed Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN
title_short Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN
title_sort automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using mask r-cnn
topic Topical Issue on Computing and Artificial Techniques for Life Science Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7968546/
https://www.ncbi.nlm.nih.gov/pubmed/33817247
http://dx.doi.org/10.1515/biol-2020-0055
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