MSM-Seg: A Modality-and-Slice Memory Framework with Category-Agnostic Prompting for Multi-Modal Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2510.10679v1
• Date: Sun, 12 Oct 2025 16:08:16 GMT
• Title: MSM-Seg: A Modality-and-Slice Memory Framework with Category-Agnostic Prompting for Multi-Modal Brain Tumor Segmentation
• Authors: Yuxiang Luo, Qing Xu, Hai Huang, Yuqi Ouyang, Zhen Chen, Wenting Duan,
• Abstract summary: Multi-modal brain tumor segmentation is critical for clinical diagnosis.<n>We propose a MSM-Seg framework for multi-modal brain tumor segmentation.<n>Our framework outperforms state-of-the-art methods in multi-modal metastases and glioma tumor segmentation.
• Score: 7.189055048337262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal brain tumor segmentation is critical for clinical diagnosis, 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 a MSM-Seg framework for multi-modal brain tumor segmentation. The MSM-Seg introduces a novel dual-memory segmentation paradigm that synergistically integrates multi-modal and inter-slice information with the efficient category-agnostic prompt for brain tumor understanding. To this end, we first devise a modality-and-slice memory attention (MSMA) to exploit the cross-modal and inter-slice relationships among the input scans. Then, we propose a multi-scale category-agnostic prompt encoder (MCP-Encoder) to provide 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.

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