The majority of state‐of‐the‐art lung segmentation algorithms in the literature do not simultaneously segment lungs, lung lobes, and airway in a single algorithm. Additionally, automated algorithms typically perform the segmentation task on a series of 2D slices, which can reduce segmentation accuracy of anatomical structures (i.e. lung lobes) that may require contextual information across all three spatial dimensions. Many existing algorithms also have not been validated on chest CTs across a wide variety of conditions to evaluate algorithm generalizability. Currently, quantification of respiratory measurements requires a radiologist, trained analyst, or technician to recognize, identify, and manually annotate anatomical landmarks such as the lung lobes or airway in the chest. A fully‐automated deep learning system may eliminate the need for manual analysis, thereby improving efficiency and expanding applicability to a large number of CTs.