Song Xinyu, Zhang Xiangyu, Li Jing, Liang Lan, Yang Yang, Li Guangjun, Bai Sen
Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, 610041.
School of Physical Science and Technology, Wuhan University, Wuhan, 430072.
Zhongguo Yi Liao Qi Xie Za Zhi. 2022 Nov 30;46(6):691-695. doi: 10.3969/j.issn.1671-7104.2022.06.021.
Adaptive radiotherapy can modify the treatment plan online based on the clinical target volume (CTV) and organ at risk (OAR) contours on the cone-beam CT (CBCT) before treatment, improving the accuracy of radiotherapy. However, manual delineation of CTV and OAR on CBCT is time-consuming. In this study, a deep neural network-based method based on U-Net was purposed. CBCT images and corresponding mask were used for model training and validation, showing superior performance in terms of the segmentation accuracy. The proposed method could be used in the clinic to support rapid CTV and OAR contouring for prostate adaptive radiotherapy.
自适应放疗可以在治疗前根据锥形束CT（CBCT）上的临床靶区（CTV）和危及器官（OAR）轮廓在线修改治疗计划，提高放疗的准确性。然而，在CBCT上手动勾画CTV和OAR很耗时。在本研究中，提出了一种基于U-Net的深度神经网络方法。使用CBCT图像和相应的掩码进行模型训练和验证，在分割精度方面表现出卓越性能。所提出的方法可用于临床，以支持前列腺自适应放疗中快速的CTV和OAR轮廓勾画。
