  The evaluation of obstructions (stenosis) in coronary arteries is currently
done by a physician's visual assessment of coronary angiography video
sequences. It is laborious, and can be susceptible to interobserver variation.
Prior studies have attempted to automate this process, but few have
demonstrated an integrated suite of algorithms for the end-to-end analysis of
angiograms. We report an automated analysis pipeline based on deep learning to
rapidly and objectively assess coronary angiograms, highlight coronary vessels
of interest, and quantify potential stenosis. We propose a 3-stage automated
analysis method consisting of key frame extraction, vessel segmentation, and
stenosis measurement. We combined powerful deep learning approaches such as
ResNet and U-Net with traditional image processing and geometrical analysis. We
trained and tested our algorithms on the Left Anterior Oblique (LAO) view of
the right coronary artery (RCA) using anonymized angiograms obtained from a
tertiary cardiac institution, then tested the generalizability of our technique
to the Right Anterior Oblique (RAO) view. We demonstrated an overall
improvement on previous work, with key frame extraction top-5 precision of
98.4%, vessel segmentation F1-Score of 0.891 and stenosis measurement 20.7%
Type I Error rate.
