Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning
Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning
复制标题
基于深度学习的鼻咽癌计算机断层扫描图像中危险器官的检测和分割，用于放射治疗计划
DOI：
10.1007/s00330-018-5748-9
复制
发表时间：
2019-04-01
影响因子：
5.9
通讯作者：
Zhang, Yu
中科院分区：
文献类型：
--
作者：
Liang, Shujun;Tang, Fan;Zhang, Yu
CT图像中危及器官（OARs）的准确检测和分割是鼻咽癌放射治疗计划制定的关键。我们在CT图像上开发了一种完全自动化的基于深度学习的方法（称为器官风险检测和分割网络（ODS网）），并研究了ODS网在自动检测和分割OARs.MethodsODS网中的性能。第一个CNN提出器官边界框沿着它们的分数，然后第二个CNN利用所提出的边界框来预测每个器官的分割掩模。本研究共纳入185例受试者进行统计学比较。进行灵敏度和特异性以确定检测性能，并使用Dice系数定量测量自动分割结果和手动分割之间的重叠。采用配对样本t检验和方差分析进行统计学分析。结果ODS网提供了一个准确的检测结果与大多数器官的灵敏度为0.997至1和特异性为0.983至0.999。此外，ODS网络的分割结果与大多数器官的Dice系数大于0.85的手动分割密切相关。ODS网络（0.861 ± 0.07）中所有器官的Dice系数（p= 0.0003 < 0.01）显著高于全卷积神经网络（FCN）（0.8 ± 0.07）。不同T分期患者之间每个OAR的Dice系数没有显著差异。结论ODS网络可以准确地自动检测和分割CT图像中的OAR，从而可以改善和促进NPC的放射治疗计划。关键点·开发了一种全自动深度学习方法（ODS net）来检测和分割临床CT图像中的OAR。·这种基于深度学习的框架可产生可靠的检测和分割结果，因此可用于在NPC放射治疗计划中描绘OAR。·这种基于深度学习的框架描绘单个图像需要大约30秒，这适用于临床工作流程。
ObjectiveAccurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (termed organs-at-risk detection and segmentation network (ODS net)) on CT images and investigate ODS net performance in automated detection and segmentation of OARs.MethodsThe ODS net consists of two convolutional neural networks (CNNs). The first CNN proposes organ bounding boxes along with their scores, and then a second CNN utilizes the proposed bounding boxes to predict segmentation masks for each organ. A total of 185 subjects were included in this study for statistical comparison. Sensitivity and specificity were performed to determine the performance of the detection and the Dice coefficient was used to quantitatively measure the overlap between automated segmentation results and manual segmentation. Paired samplesttests and analysis of variance were employed for statistical analysis.ResultsODS net provides an accurate detection result with a sensitivity of 0.997 to 1 for most organs and a specificity of 0.983 to 0.999. Furthermore, segmentation results from ODS net correlated strongly with manual segmentation with a Dice coefficient of more than 0.85 in most organs. A significantly higher Dice coefficient for all organs together (p= 0.0003 < 0.01) was obtained in ODS net (0.861 ± 0.07) than in fully convolutional neural network (FCN) (0.8 ± 0.07). The Dice coefficients of each OAR did not differ significantly between different T-staging patients.ConclusionThe ODS net yielded accurate automated detection and segmentation of OARs in CT images and thereby may improve and facilitate radiotherapy planning for NPC.Key Points• A fully automated deep-learning method(ODS net)is developed to detect and segment OARs in clinical CT images.• This deep-learning-based framework produces reliable detection and segmentation results and thus can be useful in delineating OARs in NPC radiotherapy planning.•This deep-learning-based framework delineating a single image requires approximately 30 s,which is suitable for clinical workflows.