Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks

TL;DR

This paper introduces a deep learning approach for automated emphysema subtyping and severity assessment on thoracic CT scans, achieving higher accuracy and better agreement with expert scores than previous methods.

Contribution

The study presents a novel deep neural network that automates emphysema subtyping and severity scoring, including visualization and quantification capabilities, outperforming prior approaches.

Findings

Achieved 52% accuracy in emphysema subtyping, surpassing 45% of previous methods.

Generated high-resolution activation maps for detailed visualization.

Extended predictive capabilities to include multiple emphysema subtypes.

Abstract

Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society's visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52\%, outperforming a previously published method's accuracy of 45\%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.