Brain Tissue Segmentation based on Graph Convolutional Networks

In neuroscience research, brain tissue segmentation from magnetic resonance imaging is of significant importance. A challenging issue is to provide an accurate segmentation due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. To overcome these problems, we propose a novel brain MRI segmentation algorithm, the originality of which stands on the combination of supervoxels with graph convolutional networks. Supervoxels are generated from the 3D MRI image with the help of an improved simple linear iterative clustering algorithm. A graph is then built from these supervoxels through the K nearest neighbor algorithm, before being sent to GCNs for tissues classification. The proposed method is evaluated on the two common datasets- the BrainWeb18 dataset and the Internet Brain Segmentation Repository 18 dataset. Experiments demonstrate the performance of our method and that it is better than well-known state-of-the-art methods such as FMRIB software library, statistical parametric mapping, adaptive graph filter.