Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies

• URL: http://arxiv.org/abs/2403.00257v1
• Date: Fri, 1 Mar 2024 03:45:56 GMT
• Title: Robust deep labeling of radiological emphysema subtypes using squeeze and excitation convolutional neural networks: The MESA Lung and SPIROMICS Studies
• Authors: 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
• Abstract summary: Pulmonary emphysema is the progressive, irreversible loss of lung tissue. Recent work has led to the unsupervised learning of ten spatially-informed lung texture patterns (ss) on lung CT. We present a robust 3-D squeeze-and-excitation model for supervised classification of ss CNNs and CTES on lung CT.
• Score: 34.200556207264974
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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