  Airway segmentation from chest computed tomography scans has played an
essential role in the pulmonary disease diagnosis. The computer-assisted airway
segmentation based on the U-net architecture is more efficient and accurate
compared to the manual segmentation. In this paper we employ the U$^2$-net
trained by the Dice loss function to model the airway tree from the multi-site
CT scans based on 299 training CT scans provided by the ATM'22. The derived
saliency probability map from the training is applied to the validation data to
extract the corresponding airway trees. The observation shows that the majority
of the segmented airway trees behave well from the perspective of accuracy and
connectivity. Refinements such as non-airway regions labeling and removing are
applied to certain obtained airway tree models to display the largest component
of the binary results.
