摘要
目的基于深度反卷积神经网络(DDNN)自动分割技术,探讨其在鼻咽癌靶区和危及器官(OAR)辅助人工勾画的应用价值。方法利用已完成治疗的800例鼻咽癌患者的CT信息,构建基于DDNN算法的端到端自动分割模型,选取10例新的鼻咽癌患者作为研究测试集。通过比较10名初级医师在自动勾画基础上辅助人工勾画(DLAC)与单纯人工勾画(MC)的精确度系数(DICE)、平均一致距离(MDTA)、变异系数(CV)、标准距离偏差(SDD)、勾画时间等参数以评估自动勾画的效果。结果在DLAC组,GTV、CTV的DICE分别为0.67±0.15、0.841±0.032,MDTA分别为(0.315±0.23)、(0.032±0.098) mm,显著优于MC组(P<0.001)。除脊髓、左右晶体、下颌骨外,DLAC组其他OAR的DICE高于MC组,其中下颌骨最高,视交叉最低。此外,相较MC组,DLAC组GTV、CTV、OAR的CV、SDD均显著降低(P<0.001),总勾画时间节省63.7%(P<0.001)。结论与MC相比,基于DDNN建立的DLAC能更为准确地实现鼻咽癌GTV、CTV和OAR的勾画,可大幅提高医师工作效率及勾画一致性。
Objective To evaluate the application value of deep deconvolutional neural network(DDNN)model for automatic segmentation of target volume and organs at risk(OARs)in patients with nasopharngeal carcinoma(NPC).Methods Based on the CT images of 800 NPC patients,an end-to-end automatic segmentation model was established based on DDNN algorithm.Ten newly diagnosed with NPC were allocated into the test set.Using this DDNN model,10 junior physicians contoured the region of interest(ROI)on 10 patients by using both manual contour(MC)and DDNN deep learning-assisted contour(DLAC)methods independently.The accuracy of ROI contouring was evaluated by using the DICE coefficient and mean distance to agreement(MDTA).The coefficient of variation(CV)and standard distance deviation(SDD)were rendered to measure the inter-observer variability or consistency.The time consumed for each of the two contouring methods was also compared.Results DICE values of gross target volume(GTV)and clinical target volume(CTV),MDTA of GTV and CTV by using DLAC were 0.67±0.15 and 0.841±0.032,(0.315±0.23)mm and(0.032±0.098)mm,respectively,which were significantly better than those in the MC group(all P<0.001).Except for the spinal cord,lens and mandible,DLAC improved the DICE values of the other OARs,in which mandible had the highest DICE value and optic chiasm had the lowest DICE value.Compared with the MC group,GTV,CTV,CV and SDD of OAR were significantly reduced(all P<0.001),and the total contouring time was significantly shortened by 63.7%in the DLAC group(P<0.001).Conclusion Compared with MC,DLAC is a promising method to obtain superior accuracy,consistency,and efficiency for the GTV,CTV and OAR in NPC patients.
作者
刘洋
张烨
易俊林
Liu Yang;Zhang Ye;Yi Junlin(Department of Radiation Oncology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100021,China)
出处
《中华放射肿瘤学杂志》
CSCD
北大核心
2021年第9期882-887,共6页
Chinese Journal of Radiation Oncology
关键词
自动勾画
靶区
危及器官
鼻咽肿瘤/放射疗法
Automatic segmentation
Target volume
Organ at risk
Nasopharngeal neoplasm/radiotherapy
