# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 3 Results and Discussion
complex tumor regions, maintaining high performance across different datasets, underscores its potential as a reliable tool in medical imaging segmentation. The consistency in outperforming other methods across both datasets not only highlights the effectiveness of the Spectral-Spatial approach but also marks a significant step forward in the field, suggesting further exploration into combining modalities and advanced graph theories could yield even greater advancements in tumor segmentation. In Figure 7, the precision of the Spectral-Spatial GNN model is exemplified. The depicted brain MRI slice, extracted from the BraTS dataset showcases the predicted tumor area, illustrated with heightened clarity and precision. Our findings suggest that while existing methods provide a strong foundation for brain tumor segmentation, the incorporation of graph-based neural networks offers a significant leap forward in accuracy and reliability. The Spectral-Spatial GNN model's superior performance highlights its potential as a state-of-the-art solution for brain tumor segmentation, promising to enhance diagnostic processes and patient outcomes significantly. However, to realize its full potential, several key challenges need further exploration. The complexity of supervoxel generation and the intricate graph structures that model their relationships pose a barrier to easy implementation and can increase computational costs. Additionally, Graph Neural Networks require substantial training data, and the limited availability of accurately annotated medical image data can hinder the model's ability to achieve the robust performance necessary for clinical confidence. Addressing these challenges will be crucial for unlocking the benefits of Spectral-Spatial Graph Neural Networks in everyday diagnosis and treatment planning of brain tumors. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Metric** & **Necrotic Core** & **Enhancing Tumor** & **Edema** \\ \hline Spatial GNN & Dice Score & 75.2\% & 80.5\% & 70.8\% \\  & Accuracy & 85.3\% & 88.7\% & 83.2\% \\ \hline Spectral GNN & Dice Score & 78.4\% & 82.3\% & 74.6\% \\  & Accuracy & 87.5\% & 90.1\% & 85.4\% \\ \hline Spectral–Spatial GNN & Dice Score & 85.7\% & 90.2\% & 82.5\% \\  & Accuracy & 92.6\% & 94.3\% & 90.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of GNN-based methods for tumor sub-region segmentation on the validation set. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Metric** & **Necrotic Core** & **Enhancing Tumor** & **Edema** \\ \hline Spatial GNN & Dice Score & 69.5\% & 75.2\% & 64.7\% \\  & Accuracy & 81.2\% & 85.3\% & 79.4\% \\ \hline Spectral GNN & Dice Score & 72.1\% & 78.3\% & 70.2\% \\  & Accuracy & 83.7\% & 87.5\% & 82.6\% \\ \hline Spectral–Spatial GNN & Dice Score & 80.1\% & 84.7\% & 77.5\% \\  & Accuracy & 88.4\% & 91.3\% & 87.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of GNN-based methods for tumor sub-region segmentation on the test set. Figure 7: Ground truth versus three predicted classes.