Quantitative Assessment of COVID-19 Lung Disease Severity: A Segmentation-based Approach

Abstract

We present the use of mean Hounsfield units within lungs as a metric of disease severity for the comparison of image analysis models in patients with COPD and COVID. We used this metric to assess the performance of a novel 3D global context attention network for image segmentation that produces lung masks from thoracic HRCT scans. Results showed that the mean Hounsfield units enable a detailed comparison of our 3D implementation of the GC-Net model to the V-Net segmentation algorithm. We implemented a biomimetic data augmentation strategy and used a quantitative severity metric to assess its performance. Framing our investigation around lung segmentation for patients with respiratory diseases allows analysis of the strengths and weaknesses of the implemented models in this context.Clinical Relevance -Mean Hounsfield units within the lung volume can be used as an objective measure of respiratory disease severity for the comparison of CT scan analysis algorithms.