Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
Volume delineation of organs-at risk (OARs) and target tumors is an indispensable process for creating radiotherapy treatment planning. Herein, the authors propose a lightweight deep learning framework to empower the rapid and precise volume delineation of whole-body OARs and target tumors.
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-34257-x |
| _version_ | 1828191215998205952 |
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| author | Feng Shi Weigang Hu Jiaojiao Wu Miaofei Han Jiazhou Wang Wei Zhang Qing Zhou Jingjie Zhou Ying Wei Ying Shao Yanbo Chen Yue Yu Xiaohuan Cao Yiqiang Zhan Xiang Sean Zhou Yaozong Gao Dinggang Shen |
| author_facet | Feng Shi Weigang Hu Jiaojiao Wu Miaofei Han Jiazhou Wang Wei Zhang Qing Zhou Jingjie Zhou Ying Wei Ying Shao Yanbo Chen Yue Yu Xiaohuan Cao Yiqiang Zhan Xiang Sean Zhou Yaozong Gao Dinggang Shen |
| author_sort | Feng Shi |
| collection | DOAJ |
| description | Volume delineation of organs-at risk (OARs) and target tumors is an indispensable process for creating radiotherapy treatment planning. Herein, the authors propose a lightweight deep learning framework to empower the rapid and precise volume delineation of whole-body OARs and target tumors. |
| first_indexed | 2024-04-12T08:35:11Z |
| format | Article |
| id | doaj.art-7b539ad070e747a0a435de4ce3f66f02 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-12T08:35:11Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-7b539ad070e747a0a435de4ce3f66f022022-12-22T03:40:02ZengNature PortfolioNature Communications2041-17232022-11-0113111310.1038/s41467-022-34257-xDeep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapyFeng Shi0Weigang Hu1Jiaojiao Wu2Miaofei Han3Jiazhou Wang4Wei Zhang5Qing Zhou6Jingjie Zhou7Ying Wei8Ying Shao9Yanbo Chen10Yue Yu11Xiaohuan Cao12Yiqiang Zhan13Xiang Sean Zhou14Yaozong Gao15Dinggang Shen16Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Radiation Oncology, Fudan University Shanghai Cancer CenterDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Radiation Oncology, Fudan University Shanghai Cancer CenterRadiotherapy Business Unit, Shanghai United Imaging Healthcare Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Radiotherapy Business Unit, Shanghai United Imaging Healthcare Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.Volume delineation of organs-at risk (OARs) and target tumors is an indispensable process for creating radiotherapy treatment planning. Herein, the authors propose a lightweight deep learning framework to empower the rapid and precise volume delineation of whole-body OARs and target tumors.https://doi.org/10.1038/s41467-022-34257-x |
| spellingShingle | Feng Shi Weigang Hu Jiaojiao Wu Miaofei Han Jiazhou Wang Wei Zhang Qing Zhou Jingjie Zhou Ying Wei Ying Shao Yanbo Chen Yue Yu Xiaohuan Cao Yiqiang Zhan Xiang Sean Zhou Yaozong Gao Dinggang Shen Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy Nature Communications |
| title | Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy |
| title_full | Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy |
| title_fullStr | Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy |
| title_full_unstemmed | Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy |
| title_short | Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy |
| title_sort | deep learning empowered volume delineation of whole body organs at risk for accelerated radiotherapy |
| url | https://doi.org/10.1038/s41467-022-34257-x |
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