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

• URL: http://arxiv.org/abs/2109.05580v1
• Date: Sun, 12 Sep 2021 18:16:59 GMT
• Title: A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation
• Authors: Camillo Saueressig, Adam Berkley, Reshma Munbodh, Ritambhara Singh
• Abstract summary: We present a joint graph convolution-image convolution neural network as our submission to the Brain Tumor (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) The tumorous volume identified by the GNN is further refined by a simple (voxel) convolutional neural network (CNN) which produces the final segmentation.
• Score: 1.3381749415517017
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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