MGM-3DUNet: A Multi-scale Edge Semantic-guided GraphConvolutional Sequence Method for Brain Tumor Segmentation

ZHUANG Jianjun; LI Xiang; JING Shenghua; LÜ Zhenglong

doi:10.11999/JEIT260128

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ZHUANG Jianjun, LI Xiang, JING Shenghua, LÜ Zhenglong. MGM-3DUNet: A Multi-scale Edge Semantic-guided GraphConvolutional Sequence Method for Brain Tumor Segmentation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260128

Citation:

ZHUANG Jianjun, LI Xiang, JING Shenghua, LÜ Zhenglong. MGM-3DUNet: A Multi-scale Edge Semantic-guided GraphConvolutional Sequence Method for Brain Tumor Segmentation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260128

Citation:

ZHUANG Jianjun, LI Xiang, JING Shenghua, LÜ Zhenglong. MGM-3DUNet: A Multi-scale Edge Semantic-guided GraphConvolutional Sequence Method for Brain Tumor Segmentation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260128

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MGM-3DUNet: A Multi-scale Edge Semantic-guided GraphConvolutional Sequence Method for Brain Tumor Segmentation

doi: 10.11999/JEIT260128 cstr: 32379.14.JEIT260128

1.
School of Electronics and Information Engineering, Anhui Jianzhu University, Hefei 230031, China
2.
School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
3.
Department of Radiation Oncology, Jinling Hospital, School of Medicine Nanjing University, Nanjing 210002, China

Funds: The National Key Research and Development Program of China(2021YFE0105500), The National Natural Science Foundation of China(62171228), Jiangsu Provincial University ‘Blue and Yellow Project’ Funding

Received Date: 2026-02-02
Accepted Date: 2026-05-29
Rev Recd Date: 2026-05-23

Available Online: 2026-06-10

Abstract

Abstract

Objective Feature fusion in U-Net and its 3D variants mainly relies on simple single-scale concatenation, which limits the use of encoder features and weakens fine-grained segmentation of Tumor Core (TC) and Enhancing Tumor (ET) regions. Recent methods such as VM-UNet improve sequence modeling efficiency, but they mainly focus on global information modeling. Local detail preservation and edge enhancement remain insufficient. Therefore, current methods still have limitations in segmentation accuracy and clinical utility. To address these problems, this paper proposes MGM-3DUNet for brain tumor segmentation. Methods The Multi-Scale Edge semantic Guidance Module (MEGM) is designed to improve tumor boundary segmentation through learnable edge detection. The Graph Convolutional Sequence Module (GCSM) combines the local aggregation ability of graph convolution with efficient long-range modeling based on a Mamba-like structure. This design improves semantic consistency while preserving small tumor structures with fewer parameters. The Multi-scale Context Perception Module (MCPM) is introduced to strengthen feature complementarity across different tumor scales through dual-scale fusion. Results and Discussions Experiments show that the proposed method achieves better average Dice similarity coefficient (Dice) and 95th percentile Hausdorff Distance (HD95) than the comparison methods. With only 2.3M parameters, MGM-3DUNet achieves Dice values of 91.2%, 90.4%, and 89.2% for Whole Tumor (WT), TC, and ET, respectively. The visualization results (Fig. 9, Fig. 10) further show that MEGM improves boundary localization. Overall, the proposed method shows improved sensitivity to edge details and contextual correlations while maintaining a low parameter count. Conclusions This method improves tumor boundary prediction by introducing shallow-layer edge enhancement to emphasize tumor contours. Local and global semantic information is fused in the bottleneck layer, and multi-scale contextual features are integrated during decoding. The proposed design achieves accurate segmentation with low computational cost and is suitable for deployment on resource-constrained platforms.
- 3DUNet,
- Lightweight,
- Edge semantic guidance,
- Graph convolution,
- Brain tumor segmentation

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References(29)

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