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 on the validation set by 2% across all metrics evaluated.
CITATION STYLE
Saueressig, C., Berkley, A., Munbodh, R., & Singh, R. (2022). A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12962 LNCS, pp. 356–365). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-08999-2_30
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