SDFR-Net: A Stage-Asymmetric Spectral Diffusion and Frequency–Spatial Refinement Network for Brain Tumor MRI Segmentation

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Accurate brain tumor segmentation from multi-modal magnetic resonance imaging (MRI) is essential for clinical diagnosis and treatment planning. However, effectively capturing long-range contextual information and fine lesion boundaries under limited computational budgets remains challenging. In this work, we propose SDFR-Net, a lightweight stage-asymmetric Spectral Diffusion and Frequency–Spatial Refinement Network for efficient 2.5D brain tumor MRI segmentation. Instead of applying identical processing across all hierarchical stages, SDFR-Net adopts stage-dependent spectral diffusion, stage-selective conditional refinement, and asymmetric cross-stage frequency-grid allocation to accommodate the distinct semantic and frequency characteristics of shallow and deep representations. The network consists of a Spectral Diffusion Encoder for spectral-domain contextual propagation, a Frequency–Spatial Enhancement Module for adaptive refinement of multi-scale skip features, and a lightweight Conditional Refinement Decoder for lesion-aware reconstruction. Experiments on the BraTS 2019 and BraTS 2020 datasets demonstrate that SDFR-Net achieves whole-tumor Dice scores of 0.856 and 0.880, respectively, while requiring only 1.33 M parameters. Ablation comparisons of stage-selective FiLM injection and symmetric versus asymmetric frequency-grid schedules further support the stage-asymmetric design. These results indicate that SDFR-Net provides a favorable accuracy–efficiency trade-off for resource-constrained brain tumor MRI segmentation.