Abstract

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Title:: Unsupervised deformable medical image registration based on self-similarity context and mixed attention

Author(s):: LI Bicao1; WANG Yan1; WANG Bei2; SHAO Zhuhong3; GUO Xuwei4; YI Benze1; 1. School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China; 2. Infirmary, Zhongyuan University of Technology, Zhengzhou 451191, China; 3. Information Engineering College, Capital Normal University, Beijing 100048, China; 4. Department of Pediatrics, the First Affiliated Hospital of He’nan University of Science and Technology, Luoyang 471000, China

Keywords:: deformable medical image registration; self-similarity context; mixed attention; unsupervised deep learning

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.