[1]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):256-259.[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(3):256-259.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
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 利用深度反卷积神经网络自动勾画放疗危及器官()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第3期
页码:
256-259
栏目:
医学放射物理
出版日期:
2018-03-20

文章信息/Info

Title:
 Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network
文章编号:
1005-202X(2018)03-0256-04
作者:
 门阔戴建荣
 国家癌症中心/中国医学科学院北京协和医学院肿瘤医院, 北京 100021
Author(s):
 MEN Kuo DAI Jianrong
 National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
关键词:
 深度学习自动分割放射治疗危及器官勾画深度反卷积神经网络
Keywords:
 deep learning automatic segmentation radiotherapy organs-at-risk segmentation deep deconvolutional neural network
分类号:
R815.6
DOI:
DOI:10.3969/j.issn.1005-202X.2018.03.002
文献标志码:
A
摘要:
 目的:勾画危及器官是放射治疗中非常重要的常规工作。然而,目前的人工勾画非常耗时,而且依赖于医生的知识和经验。为此,本研究提出一种深度反卷积神经网络,用于自动和精确地勾画危及器官。 方法:深度反卷积神经网络是一个用于自动分割的端到端框架。实验使用了230例头颈部患者的数据,在其中随机选择了184例作为训练集,用于调制自动分割模型的参数,其余46例用作测试集评估方法的性能。用于分割的危及器官包括脑干、脊髓、左腮腺、右腮腺、左颞叶、右颞叶、甲状腺、喉、气管9个危及器官。自动分割精度的量化指标使用戴斯相似性系数和豪斯多夫距离。 结果:所有危及器官自动分割的戴斯相似性系数值均在0.70以上(平均值为0.81),豪斯多夫距离值在5.0 mm内(平均值为4.3 mm),表明本研究提出的自动分割方法能准确地分割危及器官。 结论:利用深度反卷积神经网络建立了一种自动分割危及器官的方法,可以得到较准确的结果,为放射治疗流程自动化提供了技术支持。
Abstract:
 Objective The segmentation of organs-at-risk (OAR) is essential to radiotherapy. However, the current manual segmentation is time-consuming and dependent on the physicians’ knowledge and experience. Herein deep deconvolutional neural network (DDNN) is proposed for the automatic and accurate segmentation of OAR. Methods DDNN is an end-to-end framework for automatic segmentation. The data of 230 head and neck patients were selected in this study. Among all the selected patients, 184 cases were randomly chosen as training set used for adjusting the parameters of automatic segmentation model, and the other 46 cases were regarded as test set used for evaluating the performance of the proposed method. The OAR to be segmented included brainstem, spinal cord, left parotid, right parotid, left temporal lobe, right temporal lobe, thyroid gland, larynx and trachea. Dice similarity coefficient and Hausdorff distance were used to measure the accuracy of segmentation. Results All the Dice similarity coefficient values of OAR were higher than 0.70, with a mean value of 0.81 and the Hausdorff distance values were within 5.0 mm, with a mean value of 4.2 mm, which demonstrated that the proposed method could segment OAR accurately. Conclusion The DDNN-based automatic method for OAR segmentation achieves an accuracy segmentation result, providing technical supports for the automation of radiotherapy.

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备注/Memo

备注/Memo:
 【收稿日期】2018-01-25
【基金项目】国家自然科学基金(11605291,11475261);国家重大研发计划(2016YFC0904600)
【作者简介】门阔,博士,助理研究员, 研究方向:图像引导放射治疗,E-mail: menkuo126@126.com
【通信作者】戴建荣,博士,二级研究员,研究方向:放疗系统的优化、术中放疗技术和图像引导放疗等,E-mail: dai_jianrong@cicams.ac.cn
更新日期/Last Update: 2018-03-21