Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep
  Neural Networks
Weiyi Xie Colin Jacobs Jean-Paul Charbonnier Dirk Jan Slebos Bram van Ginneken
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.