We present a novel airway segmentation method for volumetric chest computed tomography (CT) scans and assess its performance on multiple datasets. Our method employs a 2.5D convolutional neural network (CNN) trained in a supervised manner to segment the airway voxel-by-voxel. To enhance accuracy, we utilize three adjacent slices in orthogonal directions (axial, sagittal, and coronal) and fine-tune parameters related to tree length and leakage. The gold standard for airway segmentation was generated using a semi-automated method in AVIEW. We trained and evaluated the 2.5D CNN on inspiratory thoracic CT scans from the Korean obstructive lung disease study, including subjects with normal lung function and those with chronic obstructive pulmonary disease (COPD). The reliability and practicality of our method were demonstrated across multiple datasets. In eight test datasets following the same imaging protocol, our method achieved a 92.16% detection rate, 7.74% false positive rate, and a Dice similarity coefficient of 0.8997 ± 0.0892. In 20 test datasets from the EXACT'09 challenge, our method achieved a 60.1% detection rate and a 4.56% false positive rate.