TY - GEN
T1 - UNSUPERVISED HIERARCHICAL TRANSLATION-BASED MODEL FOR MULTI-MODAL MEDICAL IMAGE REGISTRATION
AU - Dai, Xinru
AU - Ma, Tai
AU - Cai, Haibin
AU - Wen, Ying
N1 - Publisher Copyright:
© 2022 IEEE
PY - 2022
Y1 - 2022
N2 - Deformable registration of multi-modal medical images is a challenging task in medical image processing due to the differences in both appearance and structure. We propose an unsupervised hierarchical translation-based model to perform a coarse to fine registration of multi-modal medical images. The proposed model consists of three parts: a coarse registration network, a modal translation network and a fine registration network. First, the coarse registration network learns to obtain the coarse deformation field, which is applied as structure-preserving information to generate a translated image by the modal translation network. Then, the translated image as enhancing information combined with the original images are used to derive a fine deformation field in the fine registration network. Furthermore, the final deformation field is composed from the coarse and the fine deformation fields. In this way, the proposed model can learn high accurate deformation field to implement multi-modal medical image registration. Experiments on two multi-modal brain image datasets demonstrate the effectiveness of this model.
AB - Deformable registration of multi-modal medical images is a challenging task in medical image processing due to the differences in both appearance and structure. We propose an unsupervised hierarchical translation-based model to perform a coarse to fine registration of multi-modal medical images. The proposed model consists of three parts: a coarse registration network, a modal translation network and a fine registration network. First, the coarse registration network learns to obtain the coarse deformation field, which is applied as structure-preserving information to generate a translated image by the modal translation network. Then, the translated image as enhancing information combined with the original images are used to derive a fine deformation field in the fine registration network. Furthermore, the final deformation field is composed from the coarse and the fine deformation fields. In this way, the proposed model can learn high accurate deformation field to implement multi-modal medical image registration. Experiments on two multi-modal brain image datasets demonstrate the effectiveness of this model.
KW - Medical image registration
KW - deep learning
KW - modal translation
KW - multi-modal medical image
UR - https://www.scopus.com/pages/publications/85131240120
U2 - 10.1109/ICASSP43922.2022.9746324
DO - 10.1109/ICASSP43922.2022.9746324
M3 - 会议稿件
AN - SCOPUS:85131240120
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1261
EP - 1265
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
Y2 - 22 May 2022 through 27 May 2022
ER -