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 () and Dinggang Shen ()
Additional contact information
Feng Shi: Shanghai United Imaging Intelligence Co., Ltd.
Weigang Hu: Fudan University Shanghai Cancer Center
Jiaojiao Wu: Shanghai United Imaging Intelligence Co., Ltd.
Miaofei Han: Shanghai United Imaging Intelligence Co., Ltd.
Jiazhou Wang: Fudan University Shanghai Cancer Center
Wei Zhang: Shanghai United Imaging Healthcare Co., Ltd.
Qing Zhou: Shanghai United Imaging Intelligence Co., Ltd.
Jingjie Zhou: Shanghai United Imaging Healthcare Co., Ltd.
Ying Wei: Shanghai United Imaging Intelligence Co., Ltd.
Ying Shao: Shanghai United Imaging Intelligence Co., Ltd.
Yanbo Chen: Shanghai United Imaging Intelligence Co., Ltd.
Yue Yu: Shanghai United Imaging Intelligence Co., Ltd.
Xiaohuan Cao: Shanghai United Imaging Intelligence Co., Ltd.
Yiqiang Zhan: Shanghai United Imaging Intelligence Co., Ltd.
Xiang Sean Zhou: Shanghai United Imaging Intelligence Co., Ltd.
Yaozong Gao: Shanghai United Imaging Intelligence Co., Ltd.
Dinggang Shen: Shanghai United Imaging Intelligence Co., Ltd.

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it is the most time-consuming step as manual delineation is always required from radiation oncologists. Herein, we propose a lightweight deep learning framework for radiotherapy treatment planning (RTP), named RTP-Net, to promote an automatic, rapid, and precise initialization of whole-body OARs and tumors. Briefly, the framework implements a cascade coarse-to-fine segmentation, with adaptive module for both small and large organs, and attention mechanisms for organs and boundaries. Our experiments show three merits: 1) Extensively evaluates on 67 delineation tasks on a large-scale dataset of 28,581 cases; 2) Demonstrates comparable or superior accuracy with an average Dice of 0.95; 3) Achieves near real-time delineation in most tasks with

Date: 2022
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DOI: 10.1038/s41467-022-34257-x

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