Cerebrovascular Segmentation Network Based on Fast Fourier Convolution and Mamba

Purpose. Cerebrovascular segmentation is crucial for the diagnosis and treatment of cerebrovascular diseases. However, accurately extracting cerebral vessels from Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) remains challenging due to the topological complexity and anatomical variability. Methods. This paper presents a novel Y-shaped segmentation network with fast Fourier convolution and Mamba, termed F-Mamba-YNet. The network employs a dual-encoder architecture that effectively leverages the complementarity of spectral and spatial domains for achieving the fusion of multi-level features. The spectral encoder features the Fast Fourier Convolution Module, which captures high-frequency changes in vessel edges, improving segmentation completeness and connectivity. The spatial encoder incorporates a Spatial Mamba Module, which captures long-range dependencies while enhancing the spatial feature representation of cerebral vessels. Additionally, a Multi-scale Feature Selection Module in the decoder adaptively enhances discriminative features, enabling improved feature reuse. Results. Experiments demonstrate that the proposed F-Mamba-YNet achieved 86.28% and 72.24% Dice Similarity Coefficient (DSC) on the IXI-A-SegAN dataset and MIDAS dataset. Conclusions. Compared with existing algorithms, F-Mamba-YNet provided more connected and continuous segmentation results and achieved competitive performance in terms of generalization.