  Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary
disease (COPD), and is traditionally subcategorized into three subtypes
previously identified on autopsy. Unsupervised learning of emphysema subtypes
on computed tomography (CT) opens the way to new definitions of emphysema
subtypes and eliminates the need of thorough manual labeling. However, CT-based
emphysema subtypes have been limited to texture-based patterns without
considering spatial location. In this work, we introduce a standardized spatial
mapping of the lung for quantitative study of lung texture location, and
propose a novel framework for combining spatial and texture information to
discover spatially-informed lung texture patterns (sLTPs) that represent novel
emphysema subtypes. Exploiting two cohorts of full-lung CT scans from the MESA
COPD and EMCAP studies, we first show that our spatial mapping enables
population-wide study of emphysema spatial location. We then evaluate the
characteristics of the sLTPs discovered on MESA COPD, and show that they are
reproducible, able to encode standard emphysema subtypes, and associated with
physiological symptoms.
