Robust deep labeling of radiological emphysema subtypes using squeeze
  and excitation convolutional neural networks: The MESA Lung and SPIROMICS
  Studies
Artur Wysoczanski Nabil Ettehadi Soroush Arabshahi Yifei Sun Karen Hinkley Stukovsky Karol E. Watson MeiLan K. Han Erin D Michos Alejandro P. Comellas Eric A. Hoffman Andrew F. Laine R. Graham Barr Elsa D. Angelini
Pulmonary emphysema, the progressive, irreversible loss of lung tissue, is
conventionally categorized into three subtypes identifiable on pathology and on
lung computed tomography (CT) images. Recent work has led to the unsupervised
learning of ten spatially-informed lung texture patterns (sLTPs) on lung CT,
representing distinct patterns of emphysematous lung parenchyma based on both
textural appearance and spatial location within the lung, and which aggregate
into 6 robust and reproducible CT Emphysema Subtypes (CTES). Existing methods
for sLTP segmentation, however, are slow and highly sensitive to changes in CT
acquisition protocol. In this work, we present a robust 3-D
squeeze-and-excitation CNN for supervised classification of sLTPs and CTES on
lung CT. Our results demonstrate that this model achieves accurate and
reproducible sLTP segmentation on lung CTscans, across two independent cohorts
and independently of scanner manufacturer and model.