Unsupervised deformable medical image registration based on self-similarity context and mixed attention|N|
10.3969/j.issn.1005-202X.2025.03.004
- VernacularTitle:基于自相似性上下文和混合注意力的无监督可变形医学图像配准
- Author:
Bicao LI
1
;
Yan WANG
;
Bei WANG
;
Zhuhong SHAO
;
Xuwei GUO
;
Benze YI
Author Information
1. 中原工学院信息与通信工程学院,河南 郑州 450007
- Publication Type:Journal Article
- Keywords:
deformable medical image registration;
self-similarity context;
mixed attention;
unsupervised deep learning
- From:
Chinese Journal of Medical Physics
2025;42(3):305-312
- CountryChina
- Language:Chinese
-
Abstract:
To fully exploit Transformer for accurate registration,self-similarity context is used as a feature extractor to extract the semantic information of the voxel neighborhood context,using symmetric multi-scale discrete optimization with diffusion regularization to find smooth transformations for quickly calculating the point-by-point distance between descriptors.In addition,a spatial-channel Transformer based on window attention network is proposed,which combines channel,spatial attention and self-attention scheme based on(moving)window,and makes full use of the complementary advantages of these 3 attention mechanisms,enabling the network to utilize global statistical information and have strong local fitting ability.The results of comprehensive experiments on 3D brain MRI datasets of LPBA40,IXI and OASIS shows that the proposed method is superior to the commonly used registration methods(SyN,VoxelMorph,CycleMorph,ViT-V-Net and TransMorph)on several evaluation indicators,proving its effectiveness in deformable medical image registration.
