Unsupervised discovery of spatially-informed lung texture patterns for pulmonary emphysema: The MESA COPD study

Abstract

Unsupervised discovery of pulmonary emphysema subtypes offers the potential for new definitions of emphysema on lung computed tomography (CT) that go beyond the standard subtypes identified on autopsy. Emphysema subtypes can be defined on CT as a variety of textures with certain spatial prevalence. However, most existing approaches for learning emphysema subtypes on CT are limited to texture features, which are sub-optimal due to the lack of spatial information. In this work, we exploit a standardized spatial mapping of the lung and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs). Our spatial mapping is demonstrated to be a powerful tool to study emphysema spatial locations over different populations. The discovered sLTPs are shown to have high reproducibility, ability to encode standard emphysema subtypes, and significant associations with clinical characteristics.