Deep AngioKey: A Novel Approach for Objective Keyframe Extraction in Coronary Angiography Analysis

Abstract

Coronary angiography represents the GOLD standard for diagnosing and treating obstructive coronary artery disease. Its analysis relies on cardiologist?s visual assessment which is subjective and leads to inter-observer variability and different treatment strategies. Objective methods have been proposed and implemented in catheterization laboratories. However, their input is manually chosen by the cardi-ologist. In this paper, we present a new coronary angiogram keyframe extraction, Deep AngioKey. It is based on feature extraction. The frame with the highest vessel structure was identified as keyframe. Feature extraction was done using a U-Net model trained for binary vessel segmentation. Our solution surpassed existing methods and reached an overall accuracy of 98.1% in keyframe identification with an average main frame distance of 1.63.