Unsupervised Domain Adaption With Adversarial Learning (UDAA) for Emphysema Subtyping on Cardiac CT Scans: The Mesa Study

Emphysema quantification and sub-typing is actively studied on cohorts of full-lung high-resolution CT (HRCT) scans, with promising results. Transfer of quantification and classification tools to cardiac CT scans, which involve 70% of the lungs, is challenging due to lower image resolution and degradation of textural patterns. In this study, we propose an original deep-learning domain-adaptation framework to use a pre-existing dictionary of lung texture patterns (LTP), learned on gold-standard full-lung HRCT scans, to label emphysema regions on cardiac CT scans. The method exploits convolutional neural networks (CNNs) trained for: 1) supervised lung texture classification on synthetic cardiac images, and 2) adversarial learning to discriminate between real and synthetic cardiac images. Combination of the classification and adversarial tasks enables to label real cardiac CT scans, and is evaluated on the MESA cohort (N = 15,357 scans). Our results show that image features derived from the adversarial training preserve the labeling accuracy on synthetic scans. LTP histogram signatures generated on 4,315 longitudinal pairs of cardiac CT scans, show high level of consistency over time and scanner generations. The ability to robustly label emphysema texture patterns on cardiac CT scans will enable large-scale longitudinal studies over 10 years of follow-up, for better understanding of the disease progression.