Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies
Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: AstraZeneca, GlaxoSmithKline, Sanofi, Ikaria, Chiesi Farmaceutici, COPD Foundation, Novartis Pharmaceuticals Corporation, Sunovion, Regeneron Pharmaceuticals, Mylan, Forest Research Institute, Grifols, National Institutes of Health, Gillings School of Public Health, University of North Carolina at Chapel Hill, National Heart, Lung, and Blood Institute
OpenAlex ID: W4392426203