SFM-Net: Semantic Feature-Based Multi-Stage Network for Unsupervised Image Registration

It is difficult for general registration methods to establish the fine correspondence between images with complex anatomical structures. To overcome the above problem, this work presents SFM-Net, an unsupervised multi-stage semantic feature-based network. In addition to using the pixel-based similarity metrics, we propose a feature operator and emphasize a feature registration to improve the alignment of semantic related areas. Specifically, we design a two-stage training strategy, the intensity image registration stage and the semantic feature registration stage. The former is for valid semantic features learning and intensity-based coarse registration, while the latter is for semantic areas alignment, achieving fine transformation of anatomical structure. The same structure of both stages is composed of a dual-stream feature extraction module (DFEM) and a refined deformation field generation module (RDGM). Unlike the deep learning-based approaches that utilizing down-sampled encoder to extract features, DFEM constructed by dual-stream U-Net structure can capture semantic information in decoder feature for structural alignment. Different with approaches applying cascaded networks to learn deformation field, our proposed RDGM generates multi-scale deformation fields by performing a coarse-to-fine registration within a single network. Experiments on 3D brain MRI and liver CT datasets confirm that the proposed SFM-Net achieves accurate and diffeomorphic registration results, outperforming other state-of-the-art methods.