SL-SFGR: Segmentation Learning Coupling Spatial-Frequency Structure Information Enhancement for Guiding Registration

The core of medical image registration lies in the alignment of corresponding structures. Hence, the effective construction of structure information in images is crucial for guiding registration. Some methods directly introduce explicit structure priors for assisting registration training, but obtaining high-precision structure priors is intrinsically challenging. As a segmentation model for obtaining structure priors, its capability is built upon the effective construction of structure information, thus its model learning can also be used to guide registration. However, existing registration-segmentation joint methods mostly only use segmentation results at the output level to constrain registration, neglecting the guidance of segmentation learning at the feature level for registration. Moreover, most existing methods only extract features in the image spatial domain for registration, overlooking the structure information in the frequency domain that is more easily captured to guide registration. To this end, this paper proposes an innovative registration method, namely Segmentation Learning Coupling Spatial-Frequency Structure Information Enhancement for Guiding Registration (SL-SFGR). Specifically, first, a semi-supervised segmentation learning network is constructed based on the registration deformation field to introduce structure features suitable for guiding registration. Second, an adaptive feature enhancement module is built in the spatial-frequency dual domain to further strengthen the inherent structure features. Finally, Dynamic Weight Average (DWA) is utilized for joint optimization of the model. The effectiveness of the proposed method has been verified on different brain MRI datasets. The related code is available at: https://github.com/goghfan/SL-SFGR/.