Deep-Masker: A Deep Learning-Based Tool to Assess Chord Length from Murine Lung Images

Chord length is an indirect measure of alveolar size and a critical endpoint in animal models of COPD. To assess chord length, the lumens of non-alveolar structures are eliminated from measurement by various methods, including manual masking. However, manual masking is resource-intensive and can introduce variability and bias. We created a fully-automated deep-learning-based tool to mask murine lung images and assess chord length to facilitate mechanistic and therapeutic discovery in COPD called Deep-Masker. We trained the deep-learning algorithm for Deep-Masker using 1217 images from 137 mice from 12 strains exposed to room-air or cigarette-smoke for six months. We validated this algorithm against manual masking. Deep-Masker demonstrated high accuracy with an average difference in chord length compared to manual masking of -0.3 ± 1.4% (rs=0.99) for room-air exposed mice and 0.7 ± 1.9% (rs=0.99) for cigarette-smoke exposed mice. The difference between Deep-Masker and manually masked images for change in chord length due to cigarette-smoke exposure was 6.0 ± 9.2% (rs=0.95). These values exceed published estimates for inter-observer variability for manual masking (rs=0.65) and the accuracy of published algorithms by a significant margin. We validated the performance of Deep-Masker using an independent set of images. Deep-Masker can be an accurate, precise, fully-automated method to standardize chord length measurement in murine models of lung disease (available at http://47.93.0.75:8110/login).