Nature Communications (Nov 2022)
Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
- 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
Affiliations
- Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center
- Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Miaofei Han
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center
- Wei Zhang
- Radiotherapy Business Unit, Shanghai United Imaging Healthcare Co., Ltd.
- Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Jingjie Zhou
- Radiotherapy Business Unit, Shanghai United Imaging Healthcare Co., Ltd.
- Ying Wei
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Ying Shao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Yanbo Chen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Yue Yu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Xiaohuan Cao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Yiqiang Zhan
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Yaozong Gao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.
- DOI
- https://doi.org/10.1038/s41467-022-34257-x
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 13
Abstract
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.
