Medical image registration integrates information from different types of medical images to support and improve clinical diagnosis. Existing image registration approaches are mainly classified into two categories: model-driven methods and data driven methods. Model-driven methods can achieve high registration accuracy but suffer from low computational efficiency and long processing time. In contrast, data-driven methods stand out for their high efficiency, which gives them great practical value. Taking this advantage as the core basis, this paper proposes a simple unsupervised deep learning framework embedded with a multi-scale strategy. The overall network consists of two core modules: an Affine Transformation Network (AT-Net) and a multi-scale Deformable Transformation Network (DT-Net). The multi-scale design adopted in the DT-Net enables image registration at different feature scales, which effectively improves the overall registration accuracy. In addition, a dual consistency constraint is introduced into the framework to further enhance the model robustness. The entire network realizes end-to-end medical image registration. We verified the performance of the proposed method on a public dataset, with mutual information (MI) adopted as the evaluation metric. The experimental results show that our registration algorithm outperforms several mainstream methods, including Symmetric Image Normalization (SyN), VoxelMorph (VM), the coarse-to-fine deformable transformation framework for unsupervised multi-contrast MR image registration with dual consistency constraint (C-F-I-R), TransMorph and DiffuseMorph. The comparative experiments fully demonstrate that combining the multi-scale strategy with deep learning techniques is an effective solution for medical image registration tasks.
Jiting Zhang (Sun,) studied this question.