Non-iterative Pyramid Network for Unsupervised Deformable Medical Image Registration

Abstract

Large deformation is a key issue for deformable medical image registration. Decomposing a large deformation into several small deformations is an efficient solution. Current decomposition methods can be classed into iteration-based and non-iterative-based approaches. However, compared to iteration-based methods, non-iterative-based methods have faster inference times but registration accuracy gaps. To alleviate this limitation, we design a novel Non-iterative Pyramid Network (NIPNet). Firstly, Our Dual-domain Feature Extraction Module (DFEM) extracts global and local features in the frequency and spatial domains, respectively. Hence, the model considers global and local deformations. Secondly, a Multi-scale Localization Information Fusion Module (MIFM) is applied to fuse the localization information of adjacent scales to assist the current level in obtaining a more accurate deformation field. Finally, a Pyramid Self-distillation Loss (PDL) is introduced to improve the registration accuracy by treating the final deformation field as a teacher to guide the intermediate deformation field. By conducting intensive experiments on two typical 3D brain MRI datasets, we verify that the proposed NIPNet outperforms SOTA iterative-based methods and requires only a similar runtime as non-iterative approaches. Code is available at https://github.com/JXT210/NIPNet.