In Nature communications ; h5-index 260.0
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 <2 s. This framework could be utilized to accelerate the contouring process in the All-in-One radiotherapy scheme, and thus greatly shorten the turnaround time of patients.
Shi Feng, Hu Weigang, Wu Jiaojiao, Han Miaofei, Wang Jiazhou, Zhang Wei, Zhou Qing, Zhou Jingjie, Wei Ying, Shao Ying, Chen Yanbo, Yu Yue, Cao Xiaohuan, Zhan Yiqiang, Zhou Xiang Sean, Gao Yaozong, Shen Dinggang
2022-Nov-02
