Yang Xin, Li Xueyan, Zhang Xiaoting, Song Fan, Huang Sijuan, Xia Yunfei
Sun Yat- sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China.
Xinhua College of Sun Yat-sen University, Guangzhou 510520, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2020 Nov 30;40(11):1579-1586. doi: 10.12122/j.issn.1673-4254.2020.11.07.
To investigate the accuracy of automatic segmentation of organs at risk (OARs) in radiotherapy for nasopharyngeal carcinoma (NPC).
The CT image data of 147 NPC patients with manual segmentation of the OARs were randomized into the training set (115 cases), validation set (12 cases), and the test set (20 cases). An improved network based on three-dimensional (3D) Unet was established (named as AUnet) and its efficiency was improved through end-to-end training. Organ size was introduced as a priori knowledge to improve the performance of the model in convolution kernel size design, which enabled the network to better extract the features of different organs of different sizes. The adaptive histogram equalization algorithm was used to preprocess the input CT images to facilitate contour recognition. The similarity evaluation indexes, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated to verify the validity of segmentation.
DSC and HD of the test dataset were 0.86±0.02 and 4.0±2.0 mm, respectively. No significant difference was found between the results of AUnet and manual segmentation of the OARs ( > 0.05) except for the optic nerves and the optic chiasm.
AUnet, an improved deep learning neural network, is capable of automatic segmentation of the OARs in radiotherapy for NPC based on CT images, and for most organs, the results are comparable to those of manual segmentation.
探讨鼻咽癌（NPC）放射治疗中危及器官（OARs）自动分割的准确性。
将147例有OARs手动分割的NPC患者的CT图像数据随机分为训练集（115例）、验证集（12例）和测试集（20例）。建立了一种基于三维（3D）Unet的改进网络（命名为AUnet），并通过端到端训练提高其效率。在卷积核大小设计中引入器官大小作为先验知识，以提高模型性能，使网络能够更好地提取不同大小不同器官的特征。采用自适应直方图均衡化算法对输入的CT图像进行预处理，以利于轮廓识别。计算相似性评估指标，包括骰子相似系数（DSC）和豪斯多夫距离（HD），以验证分割的有效性。
测试数据集的DSC和HD分别为0.86±0.02和4.0±2.0 mm。除视神经和视交叉外，AUnet与OARs手动分割结果之间差异无统计学意义（>0.05）。
AUnet是一种改进的深度学习神经网络，能够基于CT图像对NPC放射治疗中的OARs进行自动分割，对于大多数器官，其结果与手动分割结果相当。
