  Convolutional neural networks (CNNs) are usually used as a backbone to design
methods in biomedical image segmentation. However, the limitation of receptive
field and large number of parameters limit the performance of these methods. In
this paper, we propose a graph neural network (GNN) based method named GNN-SEG
for the segmentation of brain tissues. Different to conventional CNN based
methods, GNN-SEG takes superpixels as basic processing units and uses GNNs to
learn the structure of brain tissues. Besides, inspired by the interaction
mechanism in biological vision systems, we propose two kinds of interaction
modules for feature enhancement and integration. In the experiments, we
compared GNN-SEG with state-of-the-art CNN based methods on four datasets of
brain magnetic resonance images. The experimental results show the superiority
of GNN-SEG.
