MambaFNO-NET: A Dual-Domain Framework Integrating State Space Models and Fourier Neural Operators for Brain Tumor Segmentation

Magnetic resonance imaging (MRI) is widely utilized for brain tumor segmentation, yet significant challenges persist due to intensity variations, irregular boundaries, and substantial morphological heterogeneity. Current state-of-the-art deep learning methods often struggle to capture long-range spatial dependencies, delineate fine boundary details, and efficiently process 3D volumetric data. This study introduces a novel hybrid framework that integrates state-space models with frequency-domain learning to address these limitations. The proposed model offers four primary contributions: (1) incorporation of a morphological attention block in the encoder to enhance boundary localization via dilation-erosion gradient modeling; (2) a dual-domain bottleneck module that combines Mamba-inspired sequential modeling with the Fourier Neural Operator (FNO) for efficient local and global pattern modeling with linear complexity; (3) a Feature Pyramid Network (FPN) augmented with Feature-Guided Learning (FGL) for adaptive multi-scale semantic fusion; and (4) Laplacian Pyramid decomposition to preserve high-frequency edge details. The model demonstrates state-of-the-art performance, achieving Dice Similarity Coefficients of 0.81 ± 0.05, 0.92 ± 0.02, and 0.86 ± 0.03 for Enhancing Tumor (ET), Whole Tumor (WT), and Tumor Core (TC) on the BraTS2020 dataset, respectively, with a mean Dice of 0.86 ± 0.05. MambaFNO-NET attains Hausdorff distances (HD95) of 4.21, 6.22, and 6.85 mm for ET, WT, and TC, respectively, resulting in a mean HD95 of 5.76 mm, which underscores its superior boundary localization accuracy. Overall, MambaFNO-NET delivers an efficient and accurate solution for 3D brain tumor segmentation, balancing volumetric precision with spatial boundary alignment for practical clinical deployment.