Abstract
Chronic obstructive pulmonary disease (COPD) is a common inflammatory disease associated with restricted lung airflow. Quantitative computed tomography (CT)-based bronchial measures are popularly used in COPD-related studies, which require both airway segmentation and anatomical branch labeling. This paper presents an algorithm for anatomical labeling of human airway tree branches using a novel two-step machine learning and hierarchical features. Anatomical labeling of airway branches allows standardized spatial referencing of airway phenotypes in large population-based studies. State-of-the-art anatomical labeling methods are associated with mandatory manual reviewing and correction for mislabeled branches-a time-consuming process susceptible to inter-observer variability. The new method is fully automated, and it uses hierarchical branch-level features from the current as well as ancestral and descendant branches. During the first machine learning step, it differentiates candidate anatomical branches from insignificant topological branches, often, responsible for variations in airway branching patterns. The second step is designed for lung lobe-based classification of anatomical labels for valid candidate branches. The machine learning classifiers has been designed, trained, and validated using total lung capacity (TLC) CT scans (n = 350) from the Iowa cohort of the nationwide COPDGene study during their baseline visits. One hundred TLC CT scans were used for training and validation, and a different set of 250 scans were used for testing and evaluative experiments. The new method achieved labeling accuracies of 98.4, 97.2, 92.3, 93.4, and 94.1% in the right upper, right middle, right lower, left upper, and left lower lobe, respectively, and an overall accuracy of 95.9%. For five clinically significant segmental branches, the method has achieved an accuracy of 95.2%.