Multi-class Brain Tumor Segmentation using Graph Attention Network

TL;DR

This paper presents a novel graph attention network approach for multi-class brain tumor segmentation from MRI, achieving high accuracy and efficiency by modeling MRI data as a region adjacency graph.

Contribution

It introduces a graph attention network-based model that effectively segments brain tumors from MRI by leveraging graph neural networks and region adjacency graphs, outperforming baseline models.

Findings

Achieves mean dice scores of 0.91, 0.86, 0.79 for tumor segmentation.

Reduces Hausdorff distance (HD95) by over 50% compared to baseline.

Proves effectiveness on BraTS2021 dataset with competitive results.

Abstract

Brain tumor segmentation from magnetic resonance imaging (MRI) plays an important role in diagnostic radiology. To overcome the practical issues in manual approaches, there is a huge demand for building automatic tumor segmentation algorithms. This work introduces an efficient brain tumor summation model by exploiting the advancement in MRI and graph neural networks (GNNs). The model represents the volumetric MRI as a region adjacency graph (RAG) and learns to identify the type of tumors through a graph attention network (GAT) -- a variant of GNNs. The ablation analysis conducted on two benchmark datasets proves that the proposed model can produce competitive results compared to the leading-edge solutions. It achieves mean dice scores of 0.91, 0.86, 0.79, and mean Hausdorff distances in the 95th percentile (HD95) of 5.91, 6.08, and 9.52 mm, respectively, for whole tumor, core tumor, and enhancing tumor segmentation on BraTS2021 validation dataset. On average, these performances are >6\% and >50%, compared to a GNN-based baseline model, respectively, on dice score and HD95 evaluation metrics.