A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation

TL;DR

This paper introduces a joint graph and image convolutional neural network for brain tumor segmentation, effectively combining global brain interactions and local image details to improve accuracy in the BraTS 2021 challenge.

Contribution

The novel integration of graph neural networks with convolutional neural networks enhances tumor segmentation performance over existing methods.

Findings

GNN alone effectively segments tumors

Adding CNN improves median performance by 2%

Achieved high Dice scores and low Hausdorff distances

Abstract

We present a joint graph convolution-image convolution neural network as our submission to the Brain Tumor Segmentation (BraTS) 2021 challenge. We model each brain as a graph composed of distinct image regions, which is initially segmented by a graph neural network (GNN). Subsequently, the tumorous volume identified by the GNN is further refined by a simple (voxel) convolutional neural network (CNN), which produces the final segmentation. This approach captures both global brain feature interactions via the graphical representation and local image details through the use of convolutional filters. We find that the GNN component by itself can effectively identify and segment the brain tumors. The addition of the CNN further improves the median performance of the model by 2 percent across all metrics evaluated. On the validation set, our joint GNN-CNN model achieves mean Dice scores of 0.89, 0.81, 0.73 and mean Hausdorff distances (95th percentile) of 6.8, 12.6, 28.2mm on the whole tumor, core tumor, and enhancing tumor, respectively.