2022/05/14 by Wenhao Huang, Haifan Gong, Huang, Wenhao +12 · 1 citation
Computer Science · Engineering · Medicine · #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Lung Cancer Diagnosis and Treatment #Radiomics and Machine Learning in Medical Imaging #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2205.06947
The benchmark is available at https://osf.io/pskr9/?viewonly=94fa3d87274b4095ac9a4b88cc9a1341
openalex publication_date 2022/05/14 · openalex created_date 2022/05/22 · arxiv created 2026/07/30 · arxiv updated 2026/07/31 · openalex updated_date 2026/08/02
CT-based bronchial tree analysis is essential for diagnosing lung and airway diseases, yet automatic bronchus classification remains challenging because bronchial topology varies substantially across individuals. We propose the Bronchus Classification Network (BCNet), a structure-guided framework that uses segment-level topological information from point clouds to improve voxel-level representation learning. BCNet contains two jointly trained branches: a Point-Voxel Graph Neural Network (PV-GNN) for segment classification and a Convolutional Neural Network (CNN) for voxel-wise labeling. The branches share a common convolutional backbone, allowing topology-aware supervision from the PV-GNN to enhance voxel-level features. During inference, only the CNN branch is required, so BCNet retains the computational efficiency of its CNN baseline. Experiments on BronAtlas demonstrate that BCNet outperforms state-of-the-art methods by more than 8.0% in F1-score for bronchus classification. We also introduce BronAtlas, an open-access benchmark for bronchial imaging analysis that contains high-quality voxel-wise annotations of anatomical and abnormal bronchial segments. BronAtlas provides a valuable resource for developing and evaluating advanced methods for bronchial tree analysis, disease diagnosis, and surgical planning.