Residual Fully Convolutional Deformable Registration Network for Deformable Lung CT Images

Jiaping Wu, Dacheng Zheng, Xiaoyi Feng, Xiaobiao Zhang, Zhaoqiang Xia

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Deformable registration of lung Computed Tomography (CT) is an important research topic in the field of medical image registration, which can help doctors better observe the changing pattern of lung respiratory motion of patients and is of great significance for tracking lung respiratory motion, disease diagnosis, and radiotherapy. In this paper, a fully convolutional deformable registration method with residual modules is proposed for the registration of 4D-CT images of lungs. The residual blocks are inserted into the ordinary Fully Convolutional Network (FCN) to increase the depth of the intermediate layer hence improving the feature representation ability of the network to register image pairs. At the same time, in order to improve the registration multi-scale convolution into the network. During training, unsupervised learning is used to deal with the problem of less labeled data. Experiments show that the proposed method can effectively improve registration accuracy, and the registration speed can meet the needs of practical use.

源语言英语
主期刊名Proceedings - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
出版商Institute of Electrical and Electronics Engineers Inc.
97-101
页数5
ISBN(电子版)9781665468725
DOI
出版状态已出版 - 2022
活动2022 International Conference on Image Processing and Media Computing, ICIPMC 2022 - Xi�an, 中国
期限: 27 5月 202229 5月 2022

出版系列

姓名Proceedings - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022

会议

会议2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
国家/地区中国
Xi�an
时期27/05/2229/05/22

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