Multi-view and multi-frame voting-based stenosis detection and quantification in X-ray coronary angiography

The visual assessment of X-ray coronary angiography (XCA) videos is challenging due to the complex and dynamic structure of the coronary tree. Automated interpretation can improve confidence in identifying stenoses and estimating their severity, thereby supporting clinical decision-making. However, most existing approaches rely on static images or single-view videos, limiting the use of spatial and temporal information.

We propose a multi-step pipeline for automated stenosis detection, localization, and severity estimation in XCA videos. By aggregating findings across multi-view videos, the method classifies stenotic segments throughout the coronary tree and estimates the stenosis severity. The approach is evaluated at the patient level using a dataset of 219 patients. We further compare the severity estimation performance between single- and multi-view projections to assess the importance of multi-view integration.

Our approach achieves an $F1$ -score of 64.54% for segment-wise stenosis detection and 68.09% for severity classification of correctly detected stenoses. The severity classification improves when stenoses are detected in at least two views. Compared with a state-of-the-art single-view approach, our method demonstrates superior performance in patient-level stenosis detection and severity estimation.

The proposed pipeline enables robust stenosis detection, localization, and severity assessment in XCA examinations. The results highlight the importance of incorporating both temporal and multi-view information for reliable patient-level predictions.