MGM-3DUNet: A Multi-scale Edge Semantic Guided Graph Convolutional Sequence Method for Brain Tumor Segmentation

ZHUANG Jianjun; LI Xiang; JING Shenghua; LV Zhenglong

doi:10.11999/JEIT260128

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

Citation:

ZHUANG Jianjun, LI Xiang, JING Shenghua, LV Zhenglong. MGM-3DUNet: A Multi-scale Edge Semantic Guided Graph Convolutional 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 Graph Convolutional 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: National Key Research and Development Program of China(2021YFE0105500), National Natural Science Foundation of China(62171228), “Jiangsu Provincial University ‘Blue and Yellow Project’ Funding”

Accepted Date: 2026-05-29
Rev Recd Date: 2026-05-29

Available Online: 2026-06-10

Abstract

Abstract

Objective The model feature fusion method represented by U-Net and its three 3D variants is simplistic, and the segmentation of tumor core and enhancing tumor region is insufficiently fine-grained. Recent approaches such as VM-UNet have made progress in sequence modeling efficiency, but they focus more on global information modeling, and there are still deficiencies in local detail preservation and edge enhancement. Therefore, the current methods are still limited in segmentation accuracy and clinical utility. Methods MEGM is designed to enhance the segmentation accuracy of the tumor boundary through learnable edge detection. GCSM, which combines the local aggregation ability of graph convolution with the efficient long-range modeling advantages of Mamba-like structure, enhances semantic consistency while reducing parameters, and retains small tumor structure details. MCPM is introduced to improve the complementarity of tumor features at different scales through dual-scale fusion. Results and Discussions Experiments show that the average Dice and HD95 distances of the proposed method are better than those of the comparison method. The visualization results ( Figure 9, Figure 10) qualitatively confirm that the segmentation results are more accurate after incorporating MEGM. In summary, the method proposed in this paper demonstrates enhanced sensitivity to edge details and context correlation while maintaining low parameter count, and its segmentation performance is highly robust and accurate. Conclusions This method improves the accuracy of tumor boundary prediction by introducing edge enhancement in the shallow layer to emphasize tumor contours. In the bottleneck layer, multimodal local and global semantic information is fused, while multi-scale context features are integrated during the decoding stage. This design achieves high segmentation accuracy at low computational cost and is suitable for platform deployment with low computing power.
- 3DUNet,
- Lightweight,
- Edge semantic guidance,
- Graph convolution,
- Brain tumor segmentation

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References(29)

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