前列腺癌自适应放疗中基于深度学习的CBCT临床靶区及危及器官自动勾画

Automatic Delineation of Clinical Target Volume and Organ at Risk by Deep Learning for Prostate Cancer Adaptive Radiotherapy

摘要: 自适应放射治疗能够根据治疗前锥形束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，提升放疗精度，但CBCT上的手动勾画非常耗时。该研究开发了一种基于U-Net结构的深度学习勾画模型，使用CBCT图像和对应掩模图训练模型，取得了优越的勾画精度，能够快速完成CBCT图像上的CTV、膀胱、直肠、股骨头的自动勾画，可用于临床以支持前列腺适应性放疗的快速CTV和OAR勾画。

Abstract: 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.