基于自适应Unet网络的鼻咽癌放疗危及器官自动分割方法  

Segmentation of organs at risk in nasopharyngeal cancer for radiotherapy using a selfadaptive Unet network

作　　者：杨鑫[1] 李学妍 张晓婷 宋凡 黄思娟[1] 夏云飞[1] 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;Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]中山大学肿瘤防治中心//华南肿瘤学国家重点实验室//肿瘤医学协同创新中心//广东省鼻咽癌诊治研究重点实验室,广东广州510060 [2]中山大学新华学院,广东广州510520 [3]广东工业大学,广东广州510006

出　　处：《南方医科大学学报》2020年第11期1579-1586,共8页Journal of Southern Medical University

基　　金：广东省自然科学基金(2017A030310217);广州市珠江科技新星(201710010162);大学生创新创业训练计划(202013902062,202013902065,20191390109,201813902075,201813902071,201713902050);国家863计划项目(2006AA02Z4B4)。

摘　　要：目的探讨鼻咽癌放射治疗中的危及器官(OARs)的自动分割的准确性。方法在自动分割模型研究中,经CT扫描和医生手动分割后,选取147例鼻咽癌患者的CT图像及其对应勾画的OARs结构,并对其进行完全随机化分组,分成训练集(115例)、验证集(12例)、测试集(20例)。采用自适应直方图均衡化对CT图像进行预处理。利用端到端训练提高建模效率,实现一种基于三维Unet的改进网络(AUnet),将器官大小作为先验知识引入卷积核大小设计中,使网络能自适应地提取不同大小器官的特征,从而提高模型的性能。比较自动与手动分割的DSC(Dice Similarity Coefficient)系数和豪斯多夫(HD)距离以验证AUnet网络的有效性。结果测试集的平均DSC和HD分别为0.86±0.02和4.0±2.0 mm。除视神经、视交叉外,AUnet与手动分割结果无统计学差异(P>0.05)。结论引入自适应机制后,AUnet能较为准确地实现基于CT图像对鼻咽癌的危及器官的自动分割,临床应用中可大幅度提高医生的工作效率及分割的一致性。Objective To investigate the accuracy of automatic segmentation of organs at risk(OARs)in radiotherapy for nasopharyngeal carcinoma(NPC).Methods 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.Results 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(P>0.05)except for the optic nerves and the optic chiasm.Conclusion 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.

关 键 词：深度学习 自动分割 CT图像 Unet网络 AUnet 

分 类 号：R739.63[医药卫生—肿瘤]