C2FResMorph: A high-performance framework for unsupervised 2D medical image registration

Deformable medical image registration is an important precursor task for surgical automation, while enhancing the registration performance of 2D medical images remains a challenging work. Existing methods primarily minimize the similarity loss between image pairs as the main optimization objective, leading to limited registration accuracy and a lack of pixel matching. Moreover, the scarcity of informative features in 2D images often results in overfitting on the training set, hampering generalization. To address these issues, we propose C2FResMorph, a learning-based deformable registration algorithm specifically designed for 2D medical images. C2FResMorph employs a two-stage framework that improves registration accuracy and preserves topology during deformation in a coarse-to-fine manner. Inside the framework, by leveraging the convolutional neural network's locality and the multi-head self-attention mechanism's globality, a ResMorph registration network is designed. Additionally, the integration of residual image knowledge addresses deformation folding in 2D image registration, enhancing the preservation of local structures and improving generalization. Experimental evaluations on three datasets demonstrate that C2FResMorph outperforms existing learning-based methods in terms of accuracy, generalization ability for 2D medical image registration, and also retains the efficiency advantages.