An End-to-End Deep Learning Pipeline for Emphysema Quantification Using Multi-label Learning

Abstract

We propose and validate an end-to-end deep learning pipeline employing multi-label learning as a tool for creating differential diagnoses of lung pathology as well as quantifying the extent and distribution of emphysema in chest CT images. The proposed pipeline first employs deep learning based volumetric lung segmentation using a 3D CNN to extract the entire lung out of CT images. Then, a multi-label learning model is exploited for the classification creation differential diagnoses for emphysema and then used to correlate with the emphysema diagnosed by radiologists. The five lung tissue patterns which are involved in most lung disease differential diagnoses were classified as: ground glass, fibrosis, micronodules (random, perilymphatic and centrilobular lung nodules), normal appearing lung, and emphysematous lung tissue. To the best of our knowledge, this is the first end-to-end deep learning pipeline for the creation of differential diagnoses for lung disease and the quantification of emphysema. A comparative analysis shows the performance of the proposed pipeline on two publicly available datasets.