MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation
This work addresses the need for efficient and accurate automated segmentation of heterogeneous brain tumors in 3D MRI, offering a method that balances performance and computational efficiency.
MHMamba proposes a multi-head state-space model for 3D brain tumor segmentation that improves long-range dependency modeling and multimodal training stability while maintaining linear complexity, achieving stable and significant improvements in accuracy, boundary smoothness, and sensitivity on BraTS2021 and BraTS2023 datasets.
Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience-dependent, thus necessitating efficient and stable automated segmentation methods. To address the limitations of CNNs in modeling long-range dependencies, and the heavy computational and memory overhead and inter-block contextual in coherence of Transformers in 3D MRI, this paper proposes Multi-Head Mamba (MHMamba). This method combines a U-shaped architecture with a multi-head state-space model (Mamba), splitting the channel dimension into parallel SSM heads and aggregating them with residuals. This enhances long-range representation and improves the stability of multimodal training while maintaining linear complexity. To further align statistics and enhance lesion response, we designed a channel-space calibration module for multi-head outputs and introduced an adaptive fusion mechanism at skip connections to dynamically connect global semantics with local details, thereby improving boundary consistency and the detection of small-volume lesions. We conducted experiments and ablations on BraTS2021 and BraTS2023. The results showed that MHMamba achieved stable and significant improvements in overall accuracy, boundary smoothness, and sensitivity to tumor core and small-volume enhancement areas, while preserving the linear-complexity advantage of Mamba-based modeling, thus verifying the effectiveness and versatility of the method.