UNSUPERVISED CLUSTERING OF AIRWAY TREE STRUCTURES ON HIGH-RESOLUTION CT: THE MESA LUNG STUDY.

The morphology of the proximal human airway tree is highly variable in the general population, and known variants in airway branching patterns are associated with increased risk of COPD and with polymorphisms in growth factors involved in pulmonary development. Variation in the geometry and topology of the airway tree remains incompletely characterized, and their clinical implications are not yet understood. In this work, we present an approach to unsupervised clustering of airway tree structures in Billera-Holmes-Vogtmann tree-space. We validate our pipeline on synthetic airway tree data, and apply our algorithm to identify reproducible and morphologically distinct airway tree subtypes in the MESA Lung CT cohort.