Medical Image Registration with Multi-Dilated Convolution and Quintuple Attention

Abstract

In this study, we propose a registration model using multi-dilated convolution and quintuple attention. The proposed model compensates for the difficulty of capturing global features by using multi-dilated convolution. Quintuple attention is used to capture important features by weighting them in multiple aspects. Our results are compared with those of VoxelMorph and HyperMorph based on convolution neural networks. The registration was done with the spatial transform. Compared to other models, our model outperformed existing models in several metrics.