Semantic Segmentation of Gliomas on Brain MRIs by Graph Convolutional Neural Networks

Gliomas are among the most aggressive and heterogeneous brain tumours, and their characteristics make their precise segmentation very difficult, with negative consequences in diagnosis and treatment planning. Classical pixel-based segmentation techniques often struggle with the variability and complexity of glioma occurrence. In this paper, we propose a novel graph-based segmentation method utilizing Graph Neural Networks to enhance the accuracy of glioma segmentation in MRI images. Representing MRI scans with graphs helps to capture the spatial structure and contextual information about the tumour. We evaluate our method on a standard glioma dataset and compare it with U-Net-based segmentation techniques, demonstrating that our approach outperforms traditional models across multiple metrics. The results suggest that graph-based segmentation offers a powerful alternative for medical image analysis, potentially improving clinical outcomes in brain tumour management.