[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]
点击复制

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()
分享到：

《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1; 2
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 Aidong1; 2
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.

相似文献/References:

[1]陶源,王佳飞,杜俊龙,等.基于卷积神经网络的细胞识别[J].中国医学物理学杂志,2017,34(1):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
　[J].Chinese Journal of Medical Physics,2017,34(4):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[2]仇清涛,段敬豪,巩贯忠,等.基于三维动态区域生长算法的肝脏自动分割[J].中国医学物理学杂志,2017,34(7):660.[doi:10.3969/j.issn.1005-202X.2017.07.002]
　[J].Chinese Journal of Medical Physics,2017,34(4):660.[doi:10.3969/j.issn.1005-202X.2017.07.002]
[3]邓金城,彭应林,刘常春,等. 深度卷积神经网络在放射治疗计划图像分割中的应用[J].中国医学物理学杂志,2018,35(6):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
　DENG Jincheng,PENG Yinglin,LIU Changchun,et al. Application of deep convolution neural network in radiotherapy planning image segmentation[J].Chinese Journal of Medical Physics,2018,35(4):621.[doi:DOI:10.3969/j.issn.1005-202X.2018.06.001]
[4]查雪帆,杨丰,吴俣南,等. 结合迁移学习与深度卷积网络的心电分类研究[J].中国医学物理学杂志,2018,35(11):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
　ZHA Xuefan,YANG Feng,WU Yunan,et al. ECG classification based on transfer learning and deep convolution neural network[J].Chinese Journal of Medical Physics,2018,35(4):1307.[doi:DOI:10.3969/j.issn.1005-202X.2018.11.013]
[5]宫进昌,赵尚义,王远军. 基于深度学习的医学图像分割研究进展[J].中国医学物理学杂志,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
　GONG Jinchang,ZHAO Shangyi,WANG Yuanjun.Research progress on deep learning-based medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(4):420.[doi:DOI:10.3969/j.issn.1005-202X.2019.04.010]
[6]安莹,黄能军,杨荣,等. 基于深度学习的心血管疾病风险预测模型[J].中国医学物理学杂志,2019,36(9):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
　AN Ying,HUANG Nengjun,YANG Rong,et al. Deep learning-based model for risk prediction of cardiovascular diseases[J].Chinese Journal of Medical Physics,2019,36(4):1103.[doi:DOI:10.3969/j.issn.1005-202X.2019.09.021]
[7]徐航,随力,张靖雯,等.卷积神经网络在医学图像分割中的研究进展[J].中国医学物理学杂志,2019,36(11):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
　XU Hang,SUI Li,ZHANG Jingwen,et al.Progress on convolutional neural network in medical image segmentation[J].Chinese Journal of Medical Physics,2019,36(4):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[8]李渊强,吴宇雳,杨孝平.基于级联式三维卷积神经网络的肝肿瘤自动分割[J].中国医学物理学杂志,2019,36(11):1362.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.022]
　LI Yuanqiang,WU Yuli,YANG Xiaoping.Automatic liver tumor segmentation based on cascaded 3D convolutional neural network[J].Chinese Journal of Medical Physics,2019,36(4):1362.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.022]
[9]张富利,崔德琪,王秋生,等.基于深度学习和图谱库方法自动勾画肿瘤放疗中危及器官的比较[J].中国医学物理学杂志,2019,36(12):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
　ZHANG Fuli,CUI Deqi,WANG Qiusheng,et al.Comparative study of deep learning- versus Atlas-based auto-segmentation of organs-at-risk in tumor radiotherapy[J].Chinese Journal of Medical Physics,2019,36(4):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
[10]温佳圆,林国钰,张逸文,等.应用深度学习网络实现肾小球滤过膜超微病理图像的语义分割[J].中国医学物理学杂志,2020,37(2):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
　WEN Jiayuan,LIN Guoyu,ZHANG Yiwen,et al.Semantic segmentation of ultrastructural pathological images of glomerular filtration membrane using deep learning network[J].Chinese Journal of Medical Physics,2020,37(4):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
[11]门阔,戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官[J].中国医学物理学杂志,2018,35(3):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
　MEN Kuo,DAI Jianrong. Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network[J].Chinese Journal of Medical Physics,2018,35(4):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
[12]曹洋森,朱晓斐,韩妙飞,等.基于级联式深度网络模型的胃及胰腺自动分割研究[J].中国医学物理学杂志,2021,38(8):971.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.010]
　CAO Yangsen,ZHU Xiaofei,HAN Miaofei,et al.Automatic segmentation of the stomach and pancreas using cascaded deep convolutional neural network[J].Chinese Journal of Medical Physics,2021,38(4):971.[doi:DOI:10.3969/j.issn.1005-202X.2021.08.010]

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