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Airway segmentation from chest computed tomography scans has played anessential role in the pulmonary disease diagnosis. The computer-assisted airwaysegmentation based on the U-net architecture is more efficient and accuratecompared to the manual segmentation. In this paper we employ the U$^2$-nettrained by the Dice loss function to model the airway tree from the multi-siteCT scans based on 299 training CT scans provided by the ATM'22. The derivedsaliency probability map from the training is applied to the validation data toextract the corresponding airway trees. The observation shows that the majorityof the segmented airway trees behave well from the perspective of accuracy andconnectivity. Refinements such as non-airway regions labeling and removing areapplied to certain obtained airway tree models to display the largest componentof the binary results.

A CT-Based Airway Segmentation Using U$^2$-net Trained by the Dice Loss Function