Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies
<|reference_start|>Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies: 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.<|reference_end|>
@article{wysoczanski2024robust,
    title={Robust deep labeling of radiological emphysema subtypes using squeeze
  and excitation convolutional neural networks: The MESA Lung and SPIROMICS
  Studies},
    author={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, and Elsa D.
  Angelini},
    journal={arXiv preprint arXiv:2403.00257},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00257},
    primaryClass={cs.CV cs.LG}
}