Classification and Quantification of Emphysema Using a Multi-Scale Residual Network

Abstract

Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted in this area, there still remain two major challenges. First, different emphysematous tissue appears in different scales, which we call "inter-class variations." Second, the intensities of CT images acquired from different patients, scanners or scanning protocols may vary, which we call "intra-class variations". In this paper, we present a novel multi-scale residual network with two channels of raw CT image and its differential excitation component. We incorporate multi-scale information into our networks to address the challenge of inter-class variations. In addition to the conventional raw CT image, we use its differential excitation component as a pair of inputs to handle intra-class variations. Experimental results show that our approach has superior performance over the state-of-the- art methods, achieving a classification accuracy of 93.74% on our original emphysema database. Based on the classification results, we also perform the quantitative analysis of emphysema in 50 subjects by correlating the quantitative results (the area percentage of each class) with pulmonary functions. We show that centrilobular emphysema (CLE) and panlobular emphysema (PLE) have strong correlation with the pulmonary functions and the sum of CLE and PLE can be used as a new and accurate measure of emphysema severity instead of the conventional measure (sum of all subtypes of emphysema). The correlations between the new measure and various pulmonary functions are up to |r| = 0.922 (r is correlation coefficient).