  Airway segmentation on CT scans is critical for pulmonary disease diagnosis
and endobronchial navigation. Manual extraction of airway requires strenuous
efforts due to the complicated structure and various appearance of airway. For
automatic airway extraction, convolutional neural networks (CNNs) based methods
have recently become the state-of-the-art approach. However, there still
remains a challenge for CNNs to perceive the tree-like pattern and comprehend
the connectivity of airway. To address this, we propose a voxel-connectivity
aware approach named AirwayNet for accurate airway segmentation. By
connectivity modeling, conventional binary segmentation task is transformed
into 26 tasks of connectivity prediction. Thus, our AirwayNet learns both
airway structure and relationship between neighboring voxels. To take advantage
of context knowledge, lung distance map and voxel coordinates are fed into
AirwayNet as additional semantic information. Compared to existing approaches,
AirwayNet achieved superior performance, demonstrating the effectiveness of the
network's awareness of voxel connectivity.
