AUTOMATING CARDIOMETRIC MEASUREMENTS IN CHEST X-RAYS

The analysis of the positions, shapes, and sizes of thoracicorgans is an internationally established practice for radiolo-gists. The considerable amount of time spent on manual mea-surements of roentgenographic features reveals the need fora computerized approach for the automation of these mea-surements. In this work, we introduce a new way for theannotation of the chest x-ray data in thoracic ratios estima-tion. Using a manually annotated dataset, we developed adeep learning-based approach to infer three cardiometrics inchest radiographs, namely the Cardiothoracic Ratio, Lupi Co-efficient, and Moore Coefficient. The cardiometrics of inter-est are defined as ratios of line segments drawn over chestX-rays. We encoded the line segments with landmarks andapplied an hourglass model for landmark detection. To thebest of our knowledge, this is the first study aiming to estimatetwo out of three aforementioned cardiometrics. We comparedthe performance of the proposed solution with intraobservervariability of a radiologist using the test-retest strategy with aone-year break. We found out that human performance is notequally consistent across different measurements with morethan 20% difference in the F1-score metric