Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies
Computer Vision and Pattern Recognition2024-03-04v1Machine Learning
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.
@article{arxiv.2403.00257,
title = {Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies},
author = {Artur Wysoczanski and Nabil Ettehadi and Soroush Arabshahi and Yifei Sun and Karen Hinkley Stukovsky and Karol E. Watson and MeiLan K. Han and Erin D Michos and Alejandro P. Comellas and Eric A. Hoffman and Andrew F. Laine and R. Graham Barr and Elsa D. Angelini},
journal= {arXiv preprint arXiv:2403.00257},
year = {2024}
}