Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography
This addresses a critical issue in medical imaging for clinicians, as underestimation in lung segmentation can cause diagnostic inaccuracies, though it is incremental as it builds on existing methods.
The study tackled the problem of subjectivity in lung mask creation for chest X-ray segmentation by comparing it to CT-based total lung volume, finding that current methods significantly underestimate lung regions and exclude substantial portions, potentially leading to clinical errors.
Lung mask creation lacks well-defined criteria and standardized guidelines, leading to a high degree of subjectivity between annotators. In this study, we assess the underestimation of lung regions on chest X-ray segmentation masks created according to the current state-of-the-art method, by comparison with total lung volume evaluated on computed tomography (CT). We show, that lung X-ray masks created by following the contours of the heart, mediastinum, and diaphragm significantly underestimate lung regions and exclude substantial portions of the lungs from further assessment, which may result in numerous clinical errors.