Abstract: We present extraction of tree structures, such as airways, from image data as a graph refinement task. To this end, we propose a graph auto-encoder model that uses an encoder based on graph neural networks (GNNs) to learn embeddings from input node features and a decoder to predict connections between nodes. Performance of the GNN model is compared with mean-field networks in their ability to extract airways from 3D chest CT scans.
Keywords: Graph Neural Networks, Airways
Author Affiliation: University of Copenhagen, University of Amsterdam, University Hospital of Copenhagen, Canadian Institute for Advanced Research, Erasmus MC Rotterdam