  In this research project, we put forward an advanced method for airway
segmentation based on the existent convolutional neural network (CNN) and graph
neural network (GNN). The method is originated from the vessel segmentation,
but we ameliorate it and enable the novel model to perform better for datasets
from computed tomography (CT) scans. Current methods for airway segmentation
are considering the regular grid only. No matter what the detailed model is,
including the 3-dimensional CNN or 2-dimensional CNN in three directions, the
overall graph structures are not taken into consideration. In our model, with
the neighbourhoods of airway taken into account, the graph structure is
incorporated and the segmentation of airways are improved compared with the
traditional CNN methods. We perform experiments on the chest CT scans, where
the ground truth segmentation labels are produced manually. The proposed model
shows that compared with the CNN-only method, the combination of CNN and GNN
has a better performance in that the bronchi in the chest CT scans can be
detected in most cases. In addition, the model we propose has a wide extension
since the architecture is also utilitarian in fulfilling similar aims in other
datasets. Hence, the state-of-the-art model is of great significance and highly
applicable in our daily lives.
  Keywords: Airway segmentation, Convolutional neural network, Graph neural
network
