Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography

Abstract

Introduction

The lung regions on chest X-ray segmentation masks created according to the current gold standard method for AI-driven applications are underestimated. This can be evaluated by comparison with computed tomography.

Methods

This retrospective study included data from non-contrast chest low-dose CT examinations of 55 individuals without pulmonary pathology. Synthetic X-ray images were generated by projecting a 3D CT examination onto a 2D image plane. Two experienced radiologists manually created two types of lung masks: 3D lung masks from CT examinations (ground truth for further calculations) and 2D lung masks from synthetic X-ray images (according to the current gold standard method: following the contours of other anatomical structures). Overlapping and non-overlapping lung regions covered by both types of masks were analyzed. Volume of the overlapping regions was compared with total lung volume, and volume fractions of non-overlapping lung regions in relation to the total lung volume were calculated. The performance results between the two radiologists were compared.

Results

Significant differences were observed between lung regions covered by CT and synthetic X-ray masks. The mean volume fractions of the lung regions not covered by synthetic X-ray masks for the right lung, the left lung, and both lungs were 22.8 %, 32.9 %, and 27.3 %, respectively, for Radiologist 1 and 22.7 %, 32.9 %, and 27.3 %, respectively, for Radiologist 2. There was excellent spatial agreement between the masks created by the two radiologists.

Conclusions

Lung X-ray masks created according to the current gold standard method significantly underestimate lung regions and do not cover substantial portions of the lungs.

Implications for practice

Standard lung masks fail to encompass the whole range of the lungs and significantly restrict the field of analysis in AI-driven applications, which may lead to false conclusions and diagnoses.