Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1785084911463432192 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10402756 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zengyan dctrunetautomaticsegmentationalgorithmformedicalimagesofnasopharyngealcancerinthecontextofdeeplearning AT zengpenghui dctrunetautomaticsegmentationalgorithmformedicalimagesofnasopharyngealcancerinthecontextofdeeplearning AT shenshaodong dctrunetautomaticsegmentationalgorithmformedicalimagesofnasopharyngealcancerinthecontextofdeeplearning AT liangwei dctrunetautomaticsegmentationalgorithmformedicalimagesofnasopharyngealcancerinthecontextofdeeplearning AT lijun dctrunetautomaticsegmentationalgorithmformedicalimagesofnasopharyngealcancerinthecontextofdeeplearning AT zhaozhe dctrunetautomaticsegmentationalgorithmformedicalimagesofnasopharyngealcancerinthecontextofdeeplearning AT zhangkun dctrunetautomaticsegmentationalgorithmformedicalimagesofnasopharyngealcancerinthecontextofdeeplearning AT shenchong dctrunetautomaticsegmentationalgorithmformedicalimagesofnasopharyngealcancerinthecontextofdeeplearning |