Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease

TL;DR

This paper presents an automated deep learning pipeline for analyzing coronary angiography videos to assess stenosis, aiming to improve speed and objectivity over manual evaluation.

Contribution

It introduces an integrated 3-stage deep learning method combining key frame extraction, vessel segmentation, and stenosis measurement for coronary angiogram analysis.

Findings

Key frame extraction precision of 98.4%

Vessel segmentation F1-Score of 0.891

Stenosis measurement error rate of 20.7%

Abstract

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.