Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation

The 3D multi-modal brain tumor segmentation is critical to multi-modal healthcare, and it requires accurate identification of distinct internal anatomical subregions. While the recent prompt-based segmentation paradigms enable interactive experiences for clinicians, existing methods ignore cross-modal correlations and rely on labor-intensive category-specific prompts, limiting their applicability in real-world scenarios. To address these issues, we propose the MSM-Seg, a synergistic framework for multi-modal brain tumor segmentation. The MSM-Seg introduces a dual-memory segmentation paradigm that synergistically integrates multi-modal and inter-slice information with an efficient category-agnostic prompt for brain tumor understanding. To this end, we first devise a modality-and-slice memory attention (MSMA) to exploit the complex cross-modal correlations and spatial-slice dependencies among the input scans. czThen, we propose a multi-scale category-agnostic prompt encoder (MCP-Encoder) to provide whole tumor region guidance for decoding. Moreover, we devise a modality-adaptive fusion decoder (MF-Decoder) that leverages the complementary decoding information across different modalities to improve segmentation accuracy. Extensive experiments on different MRI datasets demonstrate that our MSM-Seg framework outperforms state-of-the-art methods in multi-modal metastases and glioma tumor segmentation. The code is available at https://github.com/xq141839/MSM-Seg.