Texture analysis improves lung-tissue segmentation on high-resolution computed tomography in COVID-19.

Background: The accurate separation of lung parenchyma, ground-glass opacity (GGO), and intrapulmonary vessels on high-resolution computed tomography (HRCT) in coronavirus disease 2019 (COVID-19) is challenging. Methods: We conducted a cross-sectional study that analyzed 530 adults (20-40 years) with RT-PCR-confirmed COVID-19. For texture modeling, we sampled 597 regions of interest (ROIs) representing parenchyma, GGO, and intrapulmonary vessels. Region-of-interest-labeled HRCT patches representing parenchyma, GGO, and vessels were analyzed using first- and second-order texture features that were computed across different square window sizes (5 × 5-20 × 20 pixels). Feature selection with stepwise linear discriminant analysis yielded a three-class classifier. The primary endpoint was overall classification accuracy, with the secondary endpoints including the effect of window size and identification of the most informative features. Results: The 20 × 20-pixel window produced the highest performance, with an overall accuracy of 88.6%. Five co-occurrence-based features (average difference, inverse difference moment, co-occurrence matrix standard deviation, sum entropy, and information correlation measure 1) were the most discriminative; the majority of the errors occurred at tissue boundaries where patches spanned mixed voxels. Conclusion: Texture-based feature extraction achieved 88.6% ROI-level accuracy and can serve as a supplementary tool during radiological interpretation of chest CT.