TY - GEN
T1 - RC-Block
T2 - 2024 IEEE International Conference on Multimedia and Expo, ICME 2024
AU - Zhang, Suwei
AU - Ma, Tai
AU - Wen, Ying
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In deformable image registration, learning-based methods have demonstrated impressive performance. However, previous methods mainly focus on enhancing the capability to predict the deformation field, without fully exploring the optimal ways to update the deformation field. As a result, errors can easily accumulate during the process of updating the deformation field, thus leading to inaccurate registration results. To solve this problem, we propose RC-Block (Refinement Coefficient Block) to find the optimal way to update the deformation field, thus achieving high accuracy registration results. The proposed module introduces a novel refinement coefficient, which can leverage information from the current scale to continuously rectify the previous deformation field. In addition, RC-Block is a plug-and-play module that can be seamlessly and easily integrated into most registration methods. Extensive experimental results show that our module can effectively improve existing advanced methods' performance with minimal additional burden.
AB - In deformable image registration, learning-based methods have demonstrated impressive performance. However, previous methods mainly focus on enhancing the capability to predict the deformation field, without fully exploring the optimal ways to update the deformation field. As a result, errors can easily accumulate during the process of updating the deformation field, thus leading to inaccurate registration results. To solve this problem, we propose RC-Block (Refinement Coefficient Block) to find the optimal way to update the deformation field, thus achieving high accuracy registration results. The proposed module introduces a novel refinement coefficient, which can leverage information from the current scale to continuously rectify the previous deformation field. In addition, RC-Block is a plug-and-play module that can be seamlessly and easily integrated into most registration methods. Extensive experimental results show that our module can effectively improve existing advanced methods' performance with minimal additional burden.
KW - Convolutional Neural Network
KW - Deformable Image Registration
KW - Deformation Field
KW - Plug-and-Play Module
KW - Refinement Coefficient
UR - https://www.scopus.com/pages/publications/85206573517
U2 - 10.1109/ICME57554.2024.10688036
DO - 10.1109/ICME57554.2024.10688036
M3 - 会议稿件
AN - SCOPUS:85206573517
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2024 IEEE International Conference on Multimedia and Expo, ICME 2024
PB - IEEE Computer Society
Y2 - 15 July 2024 through 19 July 2024
ER -