[1]秦楠楠,薛旭东,吴爱林,等.基于U-net卷积神经网络的宫颈癌临床靶区和危及器官自动勾画的研究[J].中国医学物理学杂志,2020,37(4):524-528.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]
 QIN Nannan,XUE Xudong,WU Ailin,et al.Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for cervical cancer using U-net convolutional neural network[J].Chinese Journal of Medical Physics,2020,37(4):524-528.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]
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基于U-net卷积神经网络的宫颈癌临床靶区和危及器官自动勾画的研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第4期
页码:
524-528
栏目:
医学人工智能
出版日期:
2020-04-29

文章信息/Info

Title:
Automatic segmentation of clinical target volumes and organs-at-risk in radiotherapy for cervical cancer using U-net convolutional neural network
文章编号:
1005-202X(2020)04-0524-05
作者:
秦楠楠1薛旭东2吴爱林2闫冰2朱雅迪1张朋2吴爱东12
1.安徽医科大学生物医学工程学院, 安徽 合肥 230032; 2.中国科学技术大学附属第一医院放疗科, 安徽 合肥 230001
Author(s):
QIN Nannan1 XUE Xudong2 WU Ailin2 YAN Bing2 ZHU Yadi1 ZHANG Peng2 WU Aidong12
1. School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China; 2. Department of Radiation Oncology, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China
关键词:
深度学习自动分割临床靶区危及器官放射治疗U-net
Keywords:
Keywords: deep learning automatic segmentation clinical target volume organs-at-risk radiotherapy U-net
分类号:
R737.3;R319
DOI:
DOI:10.3969/j.issn.1005-202X.2020.04.023
文献标志码:
A
摘要:
目的:基于U-net卷积神经网络的深度学习方法,探讨宫颈癌放疗临床靶区和危及器官自动勾画的可行性。方法:利用U-net卷积神经网络模型搭建的端到端自动分割框架,以100例已进行IMRT治疗的宫颈癌患者CT及组织结构信息为研究对象,并随机选取其中的10例作为测试集。勾画的对象包括临床靶区(CTV)、膀胱、直肠和左、右股骨头5个部分,比较手动和自动勾画的戴斯相似性系数(DSC)和豪斯多夫距离(HD)以评估自动勾画模型的准确性。结果:4种危及器官自动勾画的DSC值都在0.833以上,平均值是0.898;HD值均在8.3 mm以内,平均值为5.3 mm;临床靶区DSC值是0.860,HD值为13.9 mm。结论:基于U-net卷积神经网络建立的自动勾画模型能较为准确地实现宫颈癌临床靶区和危及器官的自动勾画,临床应用中可大幅提高医生的工作效率及勾画的一致性。
Abstract:
Abstract: Objective To explore the feasibility of deep learning based on U-net convolutional neural network for the automatic segmentation of clinical target volumes and organs-at-risk in the radiotherapy for cervical cancer. Methods U-net convolutional neural network model was used to construct an end-to-end automatic segmentation framework. The CT and tissue structure data of 100 patients with cervical cancer who had undergone intensity-modulated radiotherapy were analyzed in this study, and 10 of the patients were randomly selected as test sets. The clinical target volume, the bladder, the rectum and the left and right femoral heads were segmented. Dice similarity coefficient (DSC) and Hausdorff distance (HD) of manual and automatic segmentations were compared to evaluate the accuracy of the automatic segmentation model. Results All the DSC of organs-at-risk was above 0.833, with an average value of 0.898; and all the HD was within 8.3 mm, with an average value of 5.3 mm. The DSC and HD of clinical target volumes were 0.860 and 13.9 mm, respectively. Conclusion The automatic segmentation model established based on U-net convolutional neural network can accurately realize the automatic segmentations of clinical target volumes and organs-at-risk in the radiotherapy for cervical cancer, and it can also greatly improve the working efficiency of doctors and the consistency of segmentations in clinical application.

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备注/Memo

备注/Memo:
【收稿日期】2019-12-09 【基金项目】国家自然科学基金青年基金(11805198);安徽省自然科学基金青年项目(1808085QH281) 【作者简介】秦楠楠,在读硕士,主要研究方向:肿瘤放射物理学,E-mail: ahmunannan@163.com 【通信作者】吴爱东,博士,正高级工程师,研究方向:放射物理,E-mail: flkaidongwu@163.com
更新日期/Last Update: 2020-04-29