  CT-based bronchial tree analysis plays an important role in the
computer-aided diagnosis for respiratory diseases, as it could provide
structured information for clinicians. The basis of airway analysis is
bronchial tree reconstruction, which consists of bronchus segmentation and
classification. However, there remains a challenge for accurate bronchial
analysis due to the individual variations and the severe class imbalance. In
this paper, we propose a region and structure prior embedded framework named
BronchusNet to achieve accurate segmentation and classification of bronchial
regions in CT images. For bronchus segmentation, we propose an adaptive hard
region-aware UNet that incorporates multi-level prior guidance of hard
pixel-wise samples in the general Unet segmentation network to achieve better
hierarchical feature learning. For the classification of bronchial branches, we
propose a hybrid point-voxel graph learning module to fully exploit bronchial
structure priors and to support simultaneous feature interactions across
different branches. To facilitate the study of bronchial analysis, we
contribute~\textbf{BRSC}: an open-access benchmark of \textbf{BR}onchus imaging
analysis with high-quality pixel-wise \textbf{S}egmentation masks and the
\textbf{C}lass of bronchial segments. Experimental results on BRSC show that
our proposed method not only achieves the state-of-the-art performance for
binary segmentation of bronchial region but also exceeds the best existing
method on bronchial branches classification by 6.9\%.
