TY - GEN
T1 - Residual Fully Convolutional Deformable Registration Network for Deformable Lung CT Images
AU - Wu, Jiaping
AU - Zheng, Dacheng
AU - Feng, Xiaoyi
AU - Zhang, Xiaobiao
AU - Xia, Zhaoqiang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - deformable image registration
KW - lung 4D-CT
KW - multi-scale convolution
KW - residual block
UR - http://www.scopus.com/inward/record.url?scp=85139227630&partnerID=8YFLogxK
U2 - 10.1109/ICIPMC55686.2022.00026
DO - 10.1109/ICIPMC55686.2022.00026
M3 - 会议稿件
AN - SCOPUS:85139227630
T3 - Proceedings - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
SP - 97
EP - 101
BT - Proceedings - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
Y2 - 27 May 2022 through 29 May 2022
ER -