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DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning

Nasopharyngeal carcinoma (NPC) is a malignant tumor that occurs in the wall of the nasopharyngeal cavity and is prevalent in Southern China, Southeast Asia, North Africa, and the Middle East. According to studies, NPC is one of the most common malignant tumors in Hainan, China, and it has the highes...

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Autores principales: Zeng, Yan, Zeng, PengHui, Shen, ShaoDong, Liang, Wei, Li, Jun, Zhao, Zhe, Zhang, Kun, Shen, Chong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402756/
https://www.ncbi.nlm.nih.gov/pubmed/37546396
http://dx.doi.org/10.3389/fonc.2023.1190075
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author Zeng, Yan
Zeng, PengHui
Shen, ShaoDong
Liang, Wei
Li, Jun
Zhao, Zhe
Zhang, Kun
Shen, Chong
author_facet Zeng, Yan
Zeng, PengHui
Shen, ShaoDong
Liang, Wei
Li, Jun
Zhao, Zhe
Zhang, Kun
Shen, Chong
author_sort Zeng, Yan
collection PubMed
description Nasopharyngeal carcinoma (NPC) is a malignant tumor that occurs in the wall of the nasopharyngeal cavity and is prevalent in Southern China, Southeast Asia, North Africa, and the Middle East. According to studies, NPC is one of the most common malignant tumors in Hainan, China, and it has the highest incidence rate among otorhinolaryngological malignancies. We proposed a new deep learning network model to improve the segmentation accuracy of the target region of nasopharyngeal cancer. Our model is based on the U-Net-based network, to which we add Dilated Convolution Module, Transformer Module, and Residual Module. The new deep learning network model can effectively solve the problem of restricted convolutional fields of perception and achieve global and local multi-scale feature fusion. In our experiments, the proposed network was trained and validated using 10-fold cross-validation based on the records of 300 clinical patients. The results of our network were evaluated using the dice similarity coefficient (DSC) and the average symmetric surface distance (ASSD). The DSC and ASSD values are 0.852 and 0.544 mm, respectively. With the effective combination of the Dilated Convolution Module, Transformer Module, and Residual Module, we significantly improved the segmentation performance of the target region of the NPC.
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spelling pubmed-104027562023-08-05 DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning Zeng, Yan Zeng, PengHui Shen, ShaoDong Liang, Wei Li, Jun Zhao, Zhe Zhang, Kun Shen, Chong Front Oncol Oncology Nasopharyngeal carcinoma (NPC) is a malignant tumor that occurs in the wall of the nasopharyngeal cavity and is prevalent in Southern China, Southeast Asia, North Africa, and the Middle East. According to studies, NPC is one of the most common malignant tumors in Hainan, China, and it has the highest incidence rate among otorhinolaryngological malignancies. We proposed a new deep learning network model to improve the segmentation accuracy of the target region of nasopharyngeal cancer. Our model is based on the U-Net-based network, to which we add Dilated Convolution Module, Transformer Module, and Residual Module. The new deep learning network model can effectively solve the problem of restricted convolutional fields of perception and achieve global and local multi-scale feature fusion. In our experiments, the proposed network was trained and validated using 10-fold cross-validation based on the records of 300 clinical patients. The results of our network were evaluated using the dice similarity coefficient (DSC) and the average symmetric surface distance (ASSD). The DSC and ASSD values are 0.852 and 0.544 mm, respectively. With the effective combination of the Dilated Convolution Module, Transformer Module, and Residual Module, we significantly improved the segmentation performance of the target region of the NPC. Frontiers Media S.A. 2023-06-30 /pmc/articles/PMC10402756/ /pubmed/37546396 http://dx.doi.org/10.3389/fonc.2023.1190075 Text en Copyright © 2023 Zeng, Zeng, Shen, Liang, Li, Zhao, Zhang and Shen https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Zeng, Yan
Zeng, PengHui
Shen, ShaoDong
Liang, Wei
Li, Jun
Zhao, Zhe
Zhang, Kun
Shen, Chong
DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
title DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
title_full DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
title_fullStr DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
title_full_unstemmed DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
title_short DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
title_sort dctr u-net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10402756/
https://www.ncbi.nlm.nih.gov/pubmed/37546396
http://dx.doi.org/10.3389/fonc.2023.1190075
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