Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Medical computer vision and Bayesian and graphical models for biomedical imaging : MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised selected papers. MCV (Workshop) (2016 : Athens, Greece), October 2016

29202136

978-3-31-961187-7, 978-3-31-961188-4

Jie Yang, Elsa D. Angelini, Benjamin M. Smith, John H. M. Austin, Eric A. Hoffman, David A. Bluemke, R. Graham Barr, Andrew F. Laine

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.