Abstract
Pulmonary emphysema is traditionally subcategorized into three subtypes, which have distinct radiological appearances on computed tomography (CT) and can help with the diagnosis of chronic obstructive pulmonary disease (COPD). Automated texture-based quantification of emphysema subtypes has been successfully implemented via supervised learning of these three emphysema subtypes. In this work, we demonstrate that unsupervised learning on a large heterogeneous database of CT scans can generate texture prototypes that are visually homogeneous and distinct, reproducible across subjects, and capable of predicting accurately the three standard radiological subtypes. These texture prototypes enable automated labeling of lung volumes, and open the way to new interpretations of lung CT scans with finer subtyping of emphysema.
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Acknowledgements
Funding provided by NIH/NHLBI R01-HL121270, R01-HL077612, RC1-HL100543, R01-HL093081 and N01-HC095159 through N01-HC-95169, UL1-RR-024156 and UL1-RR-025005.
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Yang, J. et al. (2017). Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_7
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