摘要
目的放射治疗是鼻咽癌的主要治疗方式之一,精准的肿瘤靶区分割是提升肿瘤放疗控制率和减小放疗毒性的关键因素,但常用的手工勾画时间长且勾画者之间存在差异。本文探究Deeplabv3+卷积神经网络模型用于鼻咽癌原发肿瘤放疗靶区(primary tumor gross target volume,GTVp)自动分割的可行性。方法利用Deeplabv3+网络搭建端到端的自动分割框架,以150例已进行调强放射治疗的鼻咽癌患者CT(computed tomography)影像和GTVp轮廓为研究对象,随机选取其中15例作为测试集。以戴斯相似系数(Dice similarity coefficient,DSC)、杰卡德系数(Jaccard index,JI)、平均表面距离(average surface distance,ASD)和豪斯多夫距离(Hausdorff distance,HD)为评估标准,详细比较Deeplabv3+网络模型、U-Net网络模型的自动分割结果与临床医生手工勾画的差异。结果研究发现测试集患者的平均DSC值为0.76±0.11,平均JI值为0.63±0.13,平均ASD值为(3.4±2.0)mm,平均HD值为(10.9±8.6)mm。相比U-Net模型,Deeplabv3+网络模型的平均DSC值和JI值分别提升了3%~4%,平均ASD值减小了0.4 mm,HD值无统计学差异。结论研究表明,Deeplabv3+网络模型相比U-Net模型采用了新型编码—解码网络和带孔空间金字塔网络结构,提升了分割精度,有望提高GTVp的勾画效率和一致性,但在临床实践中需仔细审核自动分割结果。
Objective Nasopharyngeal carcinoma(NPC)is a common head and neck cancer in Southeast Asia and China.In 2018,approximately 129 thousand people were diagnosed with NPC,and approximately 73 thousand people died of it.Radiotherapy has become a standard treatment method for NPC patients.Precise radiotherapy relies on the accurate delineation of tumor targets and organs-at-risk(OARs).In radiotherapy practice,these anatomical structures are usually manually delineated by radiation oncologists on a treatment-planning system(TPS).Manual delineation,however,is a time-consuming and labor-intensive process.It is also a subjective process and,hence,prone to interpractitioner variability.The NPC target segmentation is particularly challenging because of the substantial interpatient heterogeneity in tumor shape and the poorly defined tumor-to-normal tissue interface,resulting in considerable variations in gross tumor volume among physicians.Auto-segmentation methods have the potential to improve the contouring accuracy and efficiency.Different auto-segmentation methods have been reported.Nevertheless,atlas-based segmentation has long computation time and often could not account for large anatomical variations due to the uncertainty of deformable registration.Deep learning has achieved great success in computer science.It has been applied in auto-segmenting tumor targets and OARs in radiotherapy.Studies have demonstrated that the deep leaning method can perform comparably with or even better than manual segmentation for some tumor sites.In this work,we propose a Deeplabv3+model that can automatically segment high-risk primary tumor gross target volume(GTVp)in NPC radiotherapy.Method The Deeplabv3+convolutional neural network model uses an encoder-decoder structure and a spatial pyramid pooling module to complete the segmentation of high-risk primary tumor from NPC patients.The improved MobileNetV2 network is used as the network backbone,and atrous and depthwise separable convolutions are used in the encoder and decoder modules.T
作者
薛旭东
郝晓宇
石军
丁轶
魏伟
安虹
Xue Xudong;Hao Xiaoyu;Shi Jun;Ding Yi;Wei Wei;An Hong(Department of Radiation Oncology,Hubei Cancer Hospital,Wuhan 430079,China;Department of Radiation Oncology,The First Affiliated Hospital of USTC,Division of Life Sciences and Medicine,University of Science and Technology of China,Hefei 230001,China;School of Computer Science and Technology,University of Science and Technology of China,Hefei 230026,China)
出处
《中国图象图形学报》
CSCD
北大核心
2020年第10期2151-2158,共8页
Journal of Image and Graphics
基金
国家自然科学基金项目(11704108)
安徽省自然科学基金项目(1808085QH281)。
关键词
自动分割
放射治疗
卷积神经网络
原发肿瘤放疗靶区
鼻咽癌
auto-segmentation
radiotherapy
convolutional neural network(CNN)
primary tumor gross target volume(GTVp)
nasopharyngeal carcinoma(NPC)
