SGNet: Structure-Aware Graph-Based Network for Airway Semantic Segmentation

Airway semantic segmentation, which refers to segmenting airway from background and dividing it into anatomical segments, provides clinically valuable information for lung lobe analysis, pulmonary lesion localization, and comparison between different patients. It is technically challenging due to the complicated tree-like structure, individual variations, and severe class imbalance. We propose a structure-aware graph-based network (SGNet) for airway semantic segmentation directly from chest CT scans. The proposed framework consists of a feature extractor combining a multi-task U-Net with a structure-aware GCN, and an inference module comprised of two convolutional layers. The multi-task U-Net is trained to regress bifurcation landmark heatmaps, binary and semantic segmentation maps simultaneously, providing initial predictions for graph construction. By introducing irregular edges connecting voxels with the sampled points around corresponding bifurcation landmarks, the two-layer GCN incorporates the structural prior explicitly. Experiments on both public and private datasets demonstrate that the SGNet achieves superior and robust performance, even on subjects affected by severe pulmonary diseases.