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Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network

PURPOSE/OBJECTIVE(S): The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy. MATERIALS/METHODS: Computed tomography (CT) scans from 265 rectal cancer patients treated at our institution...

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Autores principales: Sha, Xue, Wang, Hui, Sha, Hui, Xie, Lu, Zhou, Qichao, Zhang, Wei, Yin, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266488/
https://www.ncbi.nlm.nih.gov/pubmed/37324028
http://dx.doi.org/10.3389/fonc.2023.1172424
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author Sha, Xue
Wang, Hui
Sha, Hui
Xie, Lu
Zhou, Qichao
Zhang, Wei
Yin, Yong
author_facet Sha, Xue
Wang, Hui
Sha, Hui
Xie, Lu
Zhou, Qichao
Zhang, Wei
Yin, Yong
author_sort Sha, Xue
collection PubMed
description PURPOSE/OBJECTIVE(S): The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy. MATERIALS/METHODS: Computed tomography (CT) scans from 265 rectal cancer patients treated at our institution were collected to train and validate automatic contouring models. The regions of CTV and OARs were delineated by experienced radiologists as the ground truth. We improved the conventional U-Net and proposed Flex U-Net, which used a register model to correct the noise caused by manual annotation, thus refining the performance of the automatic segmentation model. Then, we compared its performance with that of U-Net and V-Net. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD) were calculated for quantitative evaluation purposes. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P< 0.05). RESULTS: Our proposed framework achieved DSC values of 0.817 ± 0.071, 0.930 ± 0.076, 0.927 ± 0.03, and 0.925 ± 0.03 for CTV, the bladder, Femur head-L and Femur head-R, respectively. Conversely, the baseline results were 0.803 ± 0.082, 0.917 ± 0.105, 0.923 ± 0.03 and 0.917 ± 0.03, respectively. CONCLUSION: In conclusion, our proposed Flex U-Net can enable satisfactory CTV and OAR segmentation for rectal cancer and yield superior performance compared to conventional methods. This method provides an automatic, fast and consistent solution for CTV and OAR segmentation and exhibits potential to be widely applied for radiation therapy planning for a variety of cancers.
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spelling pubmed-102664882023-06-15 Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network Sha, Xue Wang, Hui Sha, Hui Xie, Lu Zhou, Qichao Zhang, Wei Yin, Yong Front Oncol Oncology PURPOSE/OBJECTIVE(S): The aim of this study was to improve the accuracy of the clinical target volume (CTV) and organs at risk (OARs) segmentation for rectal cancer preoperative radiotherapy. MATERIALS/METHODS: Computed tomography (CT) scans from 265 rectal cancer patients treated at our institution were collected to train and validate automatic contouring models. The regions of CTV and OARs were delineated by experienced radiologists as the ground truth. We improved the conventional U-Net and proposed Flex U-Net, which used a register model to correct the noise caused by manual annotation, thus refining the performance of the automatic segmentation model. Then, we compared its performance with that of U-Net and V-Net. The Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD) were calculated for quantitative evaluation purposes. With a Wilcoxon signed-rank test, we found that the differences between our method and the baseline were statistically significant (P< 0.05). RESULTS: Our proposed framework achieved DSC values of 0.817 ± 0.071, 0.930 ± 0.076, 0.927 ± 0.03, and 0.925 ± 0.03 for CTV, the bladder, Femur head-L and Femur head-R, respectively. Conversely, the baseline results were 0.803 ± 0.082, 0.917 ± 0.105, 0.923 ± 0.03 and 0.917 ± 0.03, respectively. CONCLUSION: In conclusion, our proposed Flex U-Net can enable satisfactory CTV and OAR segmentation for rectal cancer and yield superior performance compared to conventional methods. This method provides an automatic, fast and consistent solution for CTV and OAR segmentation and exhibits potential to be widely applied for radiation therapy planning for a variety of cancers. Frontiers Media S.A. 2023-05-18 /pmc/articles/PMC10266488/ /pubmed/37324028 http://dx.doi.org/10.3389/fonc.2023.1172424 Text en Copyright © 2023 Sha, Wang, Sha, Xie, Zhou, Zhang and Yin 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
Sha, Xue
Wang, Hui
Sha, Hui
Xie, Lu
Zhou, Qichao
Zhang, Wei
Yin, Yong
Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network
title Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network
title_full Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network
title_fullStr Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network
title_full_unstemmed Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network
title_short Clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the Flex U-Net network
title_sort clinical target volume and organs at risk segmentation for rectal cancer radiotherapy using the flex u-net network
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10266488/
https://www.ncbi.nlm.nih.gov/pubmed/37324028
http://dx.doi.org/10.3389/fonc.2023.1172424
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