首页|基于深度学习的鼻咽癌放疗临床靶区体积和危及器官自动勾画研究

基于深度学习的鼻咽癌放疗临床靶区体积和危及器官自动勾画研究

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目的 构建一种基于深度学习U-Net的鼻咽癌临床靶区体积(clinical target volume,CTV)和危及器官(organs at risk,OARs)的自动勾画方法,并与基于图谱的自动勾画方法(atlas-based auto-segmentation,ABAS)比较,进而探讨基于深度学习自动勾画方法的可行性与优越性.方法 选取 2022 年 1 月至 9 月于江苏省苏北人民医院行鼻咽癌放疗的患者的CT定位影像150 例并进行预处理,构建基于U-Net的自动勾画模型,其中 90 例作为训练数据集,10 例作为验证集,其余 50 例作为测试集,以医师手工勾画结果为金标准,计算U-Net自动勾画模型对鼻咽癌CTV和OARs的自动勾画精度,并与ABAS勾画结果进行比较.结果 U-Net的CTV和OARs(脑干、脊髓、左眼球、右眼球、左晶状体、右晶状体、左视神经、右视神经、左下颌骨、右下颌骨、左腮腺、右腮腺、左颞叶和右颞叶)的戴斯相似性系数(Dice similarity coefficient,DSC)分别为(0.76±0.03)、(0.93±0.02)、(0.92±0.03)、(0.93±0.02)、(0.94±0.03)、(0.90±0.03)、(0.91±0.02)、(0.78±0.06)、(0.77±0.05)、(0.95±0.04)、(0.95±0.02)、(0.80±0.04)、(0.81±0.03)、(0.77±0.05)和(0.76±0.04).除CTV、视神经、腮腺和颞叶外,U-Net模型自动勾画的其余器官的豪斯多夫距离(Hausdorff distance,HD)值均≤5.60 mm且重叠比(overlap ratio,OR)值均≥0.80.U-Net较ABAS模型自动勾画的各个器官的DSC更高,HD更低且OR更高(均P<0.05),勾画各个器官的耗时也更少,总体耗时降低(176.73±54.08)s(P<0.05).结论 U-Net自动勾画模型能较好实现鼻咽癌放疗CTV和OARs的自动勾画,为临床医师的勾画提供参考并提高勾画效率,以深度学习为基础的自动勾画方法具有很高的可行性和优越性.
Automatic contouring of clinical target volume and organs at risk in radiotherapy for nasopharyngeal carcinoma based on deep learning
Objective To construct a U-Net model based on deep learning to realize the automatic contouring of clinical target volume(CTV)and organs at risk(OARs)of radiotherapy plan for nasopharyngeal carcinoma(NPC)patients,and analyze its feasibility and superi-ority as compared with atlas-based auto-segmentation(ABAS)method.Methods The CT images of 150 NPC patients undergoing radio-therapy at Northern Jiangsu People's Hospital,from January to September 2022,were selected and preprocessed to construct an automatic segmentation model based on U-Net.Ninety cases were used as a training set,10 cases as a verification set and the remaining 50 cases as a test set.The contouring accuracy of the U-Net automatic contouring model for the CTV and OARs of NPC were calculated and compared with the automatic contouring module of ABAS.Results The Dice similarity coefficients(DSCs)of CTV and OARs including brainstem,spinal cord,left eye,right eye,left lens,right lens,left optic nerve,right optic nerve,left mandible,right mandible,left parotid gland,right parotid gland,left temporal lobe,and right temporal lobe,were(0.76±0.03),(0.93±0.02),(0.92±0.03),(0.93±0.02),(0.94±0.03),(0.90±0.03),(0.91±0.02),(0.78±0.06),(0.77±0.05),(0.95±0.04),(0.95±0.02),(0.80±0.04),(0.81±0.03),(0.77±0.05),and(0.76±0.04),respectively.Except for CTV,optic nerves,parotid glands and temporal lobes,the Hausdorff distances(HDs)of other organs were≤5.60 mm and the overlap ratios(ORs)were≥0.80.Compared to ABAS,U-Net had higher DSCs,lower HDs,and higher ORs for the automatic contouring of CTV and OARs(all P<0.05).U-Net also took less time to delineate each organ,and reduced the overall time con-sumption by(176.73±54.08)seconds(P<0.05).Conclusions U-Net realized the automatic contouring of CTV and OARs in NPC radio-therapy,and improved the contouring efficiency for clinicians.The automatic contouring model based on deep learning had high feasibility and advantages.

nasopharyngeal carcinomadeep learningU-Netradiotherapyautomatic contouring

钱杰伟、陈雪梅、李军、程品晶、单国平、张获、桂龙刚、柏正璐

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江苏省苏北人民医院肿瘤科,江苏 扬州 225001

南华大学核科学技术学院,湖南 衡阳 421001

浙江省肿瘤医院放射肿瘤学重点实验室,浙江 杭州 310022

鼻咽癌 深度学习 U-Net 放射治疗 自动勾画

2024

实用肿瘤杂志
浙江大学

实用肿瘤杂志

CSTPCD
影响因子:1.034
ISSN:1001-1692
年,卷(期):2024.39(6)