MSM-Seg: A Modality-and-Slice Memory Framework with Category-Agnostic Prompting for Multi-Modal Brain Tumor Segmentation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908589003636736 |
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| author | Luo, Yuxiang Xu, Qing Huang, Hai Ouyang, Yuqi Chen, Zhen Duan, Wenting |
| author_facet | Luo, Yuxiang Xu, Qing Huang, Hai Ouyang, Yuqi Chen, Zhen Duan, Wenting |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10679 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MSM-Seg: A Modality-and-Slice Memory Framework with Category-Agnostic Prompting for Multi-Modal Brain Tumor Segmentation Luo, Yuxiang Xu, Qing Huang, Hai Ouyang, Yuqi Chen, Zhen Duan, Wenting Computer Vision and Pattern Recognition 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. |
| title | MSM-Seg: A Modality-and-Slice Memory Framework with Category-Agnostic Prompting for Multi-Modal Brain Tumor Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.10679 |