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In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and global information. Our Spectral GNN implementation employs the Laplacian matrix to efficiently map tumor tissue connectivity by capturing the graph’s global structure. Consequently, this enhances the model’s precision in classifying brain tumors into distinct types: necrosis, edema, and enhancing tumor. This model underwent extensive hyper-parameter tuning to ascertain the most effective configuration for optimal segmentation performance. Our Spectral–Spatial GNN model surpasses traditional segmentation methods in accuracy for both whole tumor and sub-regions, validated by metrics such as the dice coefficient and accuracy. For the necrotic core, the Spectral–Spatial GNN model showed a 10.6% improvement over the Spatial GNN and 8% over the Spectral GNN. Enhancing tumor gains were 9.5% and 6.4%, respectively. For edema, improvements were 12.8% over the Spatial GNN and 7.3% over the Spectral GNN, highlighting its segmentation accuracy for each tumor sub-region. This superiority underscores the model’s potential in improving brain tumor segmentation accuracy, precision, and computational efficiency. |
