[1]刘远明,王小义,郭琳,等.基于深度学习分割掩膜的胸片图像配准技术及其应用[J].中国医学物理学杂志,2022,39(10):1231-1235.[doi:DOI:10.3969/j.issn.1005-202X.2022.10.009]
 LURE Fleming Yuanming,WANG Xiaoyi,GUO Lin,et al.Chest radiograph registration technique based on segmentation mask obtained by deep learning and its application[J].Chinese Journal of Medical Physics,2022,39(10):1231-1235.[doi:DOI:10.3969/j.issn.1005-202X.2022.10.009]
点击复制

基于深度学习分割掩膜的胸片图像配准技术及其应用()
分享到:

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第10期
页码:
1231-1235
栏目:
医学影像物理
出版日期:
2022-11-02

文章信息/Info

Title:
Chest radiograph registration technique based on segmentation mask obtained by deep learning and its application
文章编号:
1005-202X(2022)10-1231-05
作者:
刘远明1王小义2郭琳1夏丽 1权申文1钱令军1李宏军3
1.深圳市智影医疗科技有限公司, 广东 深圳 518000; 2.青海省第四人民医院, 青海 西宁 810007; 3.首都医科大学附属北京佑安医院, 北京 100069
Author(s):
LURE Fleming Yuanming1 WANG Xiaoyi2 GUO Lin1 XIA Li1 QUAN Shenwen1 QIAN LingJun1 LI Hongjun3
1. Shenzhen Zhiying Medical Imaging Co., Ltd. Shenzhen 518000, China 2. The Fourth Peoples Hospital of Qinghai Province, Xining 810007, China 3. Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
关键词:
胸片深度学习分割掩膜图像配准减影分析
Keywords:
Keywords: chest radiograph deep learning segmentation mask image registration subtraction analysis
分类号:
R318
DOI:
DOI:10.3969/j.issn.1005-202X.2022.10.009
文献标志码:
A
摘要:
医学图像配准技术对临床诊断治疗具有重要意义。相比传统的图像配准方法,目前基于深度学习的配准方法提高了配准的精度和速度。为了将深度学习图像配准技术应用于胸片的配准以及减影分析,本研究先采用深度学习掩膜对原始胸片进行预处理,然后以掩膜图像作为输入,以ResUNet网络作为配准框架来实现胸片图像配准,最后评估配准效果。结果显示深度学习掩膜结合深度学习图像配准方法训练出的模型在胸片配准上具有良好的图像配准精度。这种基于掩膜的深度学习配准模型可以较好地应用于胸片的减影分析。
Abstract:
Abstract: Medical image registration technique is of vital importance for clinical diagnosis and treatment. The deep learning based registration method improves the accurate and speed of registration when compared with conventional registration methods. In order to apply deep learning algorithm to do chest radiograph registration and subsequent subtraction analysis, the original chest radiographs is preprocessed with the use of the mask obtained by deep learning, and the chest radiograph registration is achieved with mask images as input, ResUnet as registration structure. The evaluation of the registration results showed that the model developed by mask and registration technique based on deep learning has high image registration accuracy in chest radiograph registration. The proposed registration model can be well applied to the subtraction analysis of chest radiographs.

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(10):53.[doi:10.3969/j.issn.1005-202X.2017.01.011]
[2]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(10):256.[doi:DOI:10.3969/j.issn.1005-202X.2018.03.002]
[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(10):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(10):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(10):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(10):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(10):1302.[doi:DOI:10.3969/j.issn.1005-202X.2019.11.011]
[8]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(10):1486.[doi:DOI:10.3969/j.issn.1005-202X.2019.12.024]
[9]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(10):195.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.012]
[10]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.[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(10):524.[doi:DOI:10.3969/j.issn.1005-202X.2020.04.023]
[11]侯鹏飞,沈秀明,袁明远,等.基于深度学习方法的胸片异物检测[J].中国医学物理学杂志,2021,38(12):1518.[doi:DOI:10.3969/j.issn.1005-202X.2021.12.011]
 HOU Pengfei,SHEN Xiuming,et al.Detecting foreign objects in chest radiographs based on a deep learning method[J].Chinese Journal of Medical Physics,2021,38(10):1518.[doi:DOI:10.3969/j.issn.1005-202X.2021.12.011]

备注/Memo

备注/Memo:
【收稿日期】2022-05-26 【基金项目】国家重点研发计划(2019YFE0121400);深圳市科技计划项目(KQTD2017033110081833, JCYJ20190813153413160, JSGG20201102162802008) 【作者简介】刘远明,博士,教授,研究方向:机器学习与人工智能神经网络,E-mail: f.lure@hotmail.com;王小义,主任医师,研究方向:感染与炎症放射学,E-mail: qhssyfsk@126.com(刘远明和王小义共为第一作者) 【通信作者】李宏军,博士,主任医师,研究方向:感染与炎症,E-mail: lihongjun00113@126.com;郭琳,博士,研究方向:人工智能与医学影像,E-mail: guolin913@outlook.com
更新日期/Last Update: 2022-10-27