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

Automated airway segmentation by learning graphical structure