Affine medical image registration with fusion feature mapping in local and global

Abstract

Abstract Objective. Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible with most real-time medical applications. On the other hand, convolutional neural networks are limited in modeling long-range spatial relationships of the features due to inductive biases, such as weight sharing and locality. This is not conducive to affine registration tasks. Therefore, the evolution of real-time and high-accuracy affine medical image registration algorithms is necessary for registration applications. Approach. In this paper, we propose a deep learning-based coarse-to-fine global and local feature fusion architecture for fast affine registration, and we use an unsupervised approach for end-to-end training. We use multiscale convolutional kernels as our elemental convolutional blocks to enhance feature extraction. Then, to learn the long-range spatial relationships of the features, we propose a new affine registration framework with weighted global positional attention that fuses global feature mapping and local feature mapping. Moreover, the fusion regressor is designed to generate the affine parameters. Main results. The additive fusion method can be adaptive to global mapping and local mapping, which improves affine registration accuracy without the center of mass initialization. In addition, the max pooling layer and the multiscale convolutional kernel coding module increase the ability of the model in affine registration. Significance. We validate the effectiveness of our method on the OASIS dataset with 414 3D MRI brain maps. Comprehensive results demonstrate that our method achieves state-of-the-art affine registration accuracy and very efficient runtimes.