Yu Xinhao, Jin Fu, Luo HuanLi, Lei Qianqian, Wu Yongzhong
College of Bioengineering, 47913Chongqing University, Chongqing, China.
Department of radiation oncology, 605425Chongqing University Cancer Hospital, Chongqing, China.
Technol Cancer Res Treat. 2022 Jan-Dec;21:15330338221090847. doi: 10.1177/15330338221090847.
Radiotherapy is one of the most effective ways to treat lung cancer. Accurately delineating the gross target volume is a key step in the radiotherapy process. In current clinical practice, the target area is still delineated manually by radiologists, which is time-consuming and laborious. However, these problems can be better solved by deep learning-assisted automatic segmentation methods.
In this paper, a 3D CNN model named 3D ResSE-Unet is proposed for gross tumor volume segmentation for stage III NSCLC radiotherapy. This model is based on 3D Unet and combines residual connection and channel attention mechanisms. Three-dimensional convolution operation and encoding-decoding structure are used to mine three-dimensional spatial information of tumors from computed tomography data. Inspired by ResNet and SE-Net, residual connection and channel attention mechanisms are used to improve segmentation performance. A total of 214 patients with stage III NSCLC were collected selectively and 148 cases were randomly selected as the training set, 30 cases as the validation set, and 36 cases as the testing set. The segmentation performance of models was evaluated by the testing set. In addition, the segmentation results of different depths of 3D Unet were analyzed. And the performance of 3D ResSE-Unet was compared with 3D Unet, 3D Res-Unet, and 3D SE-Unet.
Compared with other depths, 3D Unet with four downsampling depths is more suitable for our work. Compared with 3D Unet, 3D Res-Unet, and 3D SE-Unet, 3D ResSE-Unet can obtain superior results. Its dice similarity coefficient, 95th-percentile of Hausdorff distance, and average surface distance can reach 0.7367, 21.39mm, 4.962mm, respectively. And the average time cost of 3D ResSE-Unet to segment a patient is only about 10s.
The method proposed in this study provides a new tool for GTV auto-segmentation and may be useful for lung cancer radiotherapy.
放射治疗是治疗肺癌最有效的方法之一。准确勾画大体肿瘤体积是放射治疗过程中的关键步骤。在当前临床实践中，目标区域仍由放射科医生手动勾画，既耗时又费力。然而，深度学习辅助的自动分割方法可以更好地解决这些问题。
本文提出了一种名为3D ResSE-Unet的3D卷积神经网络模型，用于III期非小细胞肺癌放射治疗的大体肿瘤体积分割。该模型基于3D Unet，结合了残差连接和通道注意力机制。利用三维卷积运算和编码-解码结构从计算机断层扫描数据中挖掘肿瘤的三维空间信息。受ResNet和SE-Net的启发，采用残差连接和通道注意力机制来提高分割性能。选择性收集了214例III期非小细胞肺癌患者，随机选取148例作为训练集，30例作为验证集，36例作为测试集。通过测试集评估模型的分割性能。此外，分析了不同深度的3D Unet的分割结果。并将3D ResSE-Unet的性能与3D Unet、3D Res-Unet和3D SE-Unet进行了比较。
与其他深度相比，具有四个下采样深度的3D Unet更适合我们的工作。与3D Unet、3D Res-Unet和3D SE-Unet相比，3D ResSE-Unet能获得更优的结果。其骰子相似系数、豪斯多夫距离第95百分位数和平均表面距离分别可达0.7367、21.39mm、4.962mm。并且3D ResSE-Unet分割一名患者的平均时间成本仅约为10秒。
本研究提出的方法为大体肿瘤体积自动分割提供了一种新工具，可能对肺癌放射治疗有用。
