Uncertainty in automated stenosis quantification using multiview x-ray coronary angiography videos

Visual coronary stenosis localization and severity estimation in x-ray coronary angiography (XCA) videos is a challenging task, complicated by complex vessel structure, low image quality and heart movement. This work presents a novel workflow to automate the assessment of coronary artery disease considering multiple XCA videos with different projection angles per patient. The workflow consists of five steps for XCA video processing: selection of time frames with sufficient vessel lumen visualization, detection of stenotic regions and the corresponding coronary segment in the selected frame, calculation of the stenosis degree, movement tracking to combine detections showing the same stenosis in one video, prediction of the coronary segment and stenosis degree for the stenosis represented by a set of assigned detections. We evaluated the prediction and the corresponding degree estimation for each coronary segment on patient-level and investigated the impact of considering multiple projections per patient on the stenosis evaluation accuracy.