Image registration combining cross-scale point matching and multi-scale feature fusion

Image registration plays an important role in computer-aided diagnosis of brain diseases and remote surgery. The U-Net and its variants have been widely used in the field of medical image registration, , achieving good results in registration accuracy and time. However, , existing registration models have difficulty in learning the edge features of small structures in complex image deformations and ignore the correlation of contextual information at different scales. To address these issues, , a registration model is proposed based on cross-scale point matching combined with multi-scale feature fusion. Firstly, , a cross-scale point matching module is introduced into encoding structure of the model to enhance the representation of prominent region features and grasp the edge details of small structure features. Then, , multi-scale features are fused in the decoding structure to form a more comprehensive feature description. Finally, , an attention module is integrated into the multi-scale feature fusion module to highlight spatial and channel information. The experimental results on three brain Magnetic Resonance (MR ) datasets show that, , taking the OASIS-3 dataset as an example, , the registration accuracy has been improved by 23. 5%, , 12. 4%, , 0. 9%, , and 2. 1% compared to methods such as Affine, SyN, VoxelMorph and CycleMorph, , respectively. The corresponding ASD values for each method have decreased by 1. 074, 0. 434, 0. 043, and 0. 076. The proposed model can better grasp the feature information of images, , which improves registration accuracy and has important implications for the development of medical image registration.