Enhanced Generative Model for Unsupervised Discovery of Spatially-Informed Macroscopic Emphysema: The Mesa Copd Study

Pulmonary emphysema, overlapping with Chronic Obstructive Pulmonary Disorder (COPD), contributes to a significant amount of morbidity and mortality annually. Computed tomography is used for in vivo quantification of emphysema and labeling into three standard subtypes at a macroscopic level. Unsupervised learning of texture patterns has great potential to discover more radiological emphysema subtypes. In this work, we improve a probabilistic Latent Dirichlet Allocation (LDA) model to discover spatially-informed lung macroscopic patterns (sLMPs) from previously learned spatially-informed lung texture patterns (sLTPs). We exploit a specific reproducibility metric to empirically tune the number of sLMPs and the size of patches. Experimental results on the MESA COPD cohort show that our algorithm can discover highly reproducible sLMPs, which are able to capture relationships between sLTPs and preferred localizations within the lung. The discovered sLMPs also achieve higher prediction accuracy of three standard emphysema subtypes than in our previous implementation.