Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study
This work addresses the need for automated and finer subtyping of emphysema in COPD diagnosis, offering a method that is reproducible and interpretable for medical imaging analysis.
The paper tackled the problem of automatically quantifying pulmonary emphysema subtypes from CT scans by using unsupervised learning to generate texture prototypes, achieving accurate prediction of the three standard radiological subtypes.
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.