  We present an end-to-end deep learning segmentation method by combining a 3D
UNet architecture with a graph neural network (GNN) model. In this approach,
the convolutional layers at the deepest level of the UNet are replaced by a
GNN-based module with a series of graph convolutions. The dense feature maps at
this level are transformed into a graph input to the GNN module. The
incorporation of graph convolutions in the UNet provides nodes in the graph
with information that is based on node connectivity, in addition to the local
features learnt through the downsampled paths. This information can help
improve segmentation decisions. By stacking several graph convolution layers,
the nodes can access higher order neighbourhood information without substantial
increase in computational expense. We propose two types of node connectivity in
the graph adjacency: i) one predefined and based on a regular node
neighbourhood, and ii) one dynamically computed during training and using the
nearest neighbour nodes in the feature space. We have applied this method to
the task of segmenting the airway tree from chest CT scans. Experiments have
been performed on 32 CTs from the Danish Lung Cancer Screening Trial dataset.
We evaluate the performance of the UNet-GNN models with two types of graph
adjacency and compare it with the baseline UNet.
