Artificial Intelligence (AI) tools provide rapid analysis of complex datasets, at the cost of flexibility in the data that is fed to them. To have the best performance, AI tools require training on data similar to the data that will be encountered in their clinical use. For Computed Tomography (CT) scans, a wide variety in scanning protocols imposes challenges on the use of pre-trained AI tools. In the case of airway segmentation, the required training data are complete airway segmentations. However, complete and high quality manual segmentations of airways are time consuming and prone to errors . Thus, our aim was to develop a workflow for obtaining high-quality ground truth segmentations for the purpose of training AI tools.