Graph refinement based airway extraction using mean-field networks and graph neural networks

•Extraction of tree-like structures formulated as a graph refinement task.•Propose two graph refinement strategies to extract airways from 3D data.•Demonstrate approximate inference using mean-field networks for airway extraction.•Novel application of graph neural networks for airway extraction.•Competitive results when compared to relevant baseline methods. [Display omitted] Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph refinement task. We present two methods to perform graph refinement. First, we use mean-field approximation (MFA) to approximate the posterior density over the subgraphs from which the optimal subgraph of interest can be estimated. Mean field networks (MFNs) are used for inference based on the interpretation that iterations of MFA can be seen as feed-forward operations in a neural network. This allows us to learn the model parameters using gradient descent. Second, we present a supervised learning approach using graph neural networks (GNNs) which can be seen as generalisations of MFNs. Subgraphs are obtained by training a GNN-based graph refinement model to directly predict edge probabilities. We discuss connections between the two classes of methods and compare them for the task of extracting airways from 3D, low-dose, chest CT data. We show that both the MFN and GNN models show significant improvement when compared to one baseline method, that is similar to a top performing method in the EXACT’09 Challenge, and a 3D U-Net based airway segmentation model, in detecting more branches with fewer false positives.