  High-resolution full lung CT scans now enable the detailed segmentation of
airway trees up to the 6th branching generation. The airway binary masks
display very complex tree structures that may encode biological information
relevant to disease risk and yet remain challenging to exploit via traditional
methods such as meshing or skeletonization. Recent clinical studies suggest
that some variations in shape patterns and caliber of the human airway tree are
highly associated with adverse health outcomes, including all-cause mortality
and incident COPD. However, quantitative characterization of variations
observed on CT segmented airway tree remain incomplete, as does our
understanding of the clinical and developmental implications of such. In this
work, we present an unsupervised deep-learning pipeline for feature extraction
and clustering of human airway trees, learned directly from projections of 3D
airway segmentations. We identify four reproducible and clinically distinct
airway sub-types in the MESA Lung CT cohort.
