Deep Diagnostics: Applying Convolutional Neural Networks for Vessels Defects Detection

TL;DR

This paper presents a convolutional neural network-based algorithm for automatic segmentation and defect detection in coronary angiograms, aiming to automate and improve the accuracy of diagnosing coronary artery disease.

Contribution

The authors developed a CNN and U-Net based algorithm for vessel segmentation and defect detection, demonstrating high accuracy and potential for clinical application.

Findings

High-quality vessel segmentation achieved with U-Net.

Good accuracy in ischemia evaluation on test data.

Potential to automate and speed up cardiovascular diagnosis.

Abstract

Coronary angiography is considered to be a safe tool for the evaluation of coronary artery disease and perform in approximately 12 million patients each year worldwide. [1] In most cases, angiograms are manually analyzed by a cardiologist. Actually, there are no clinical practice algorithms which could improve and automate this work. Neural networks show high efficiency in tasks of image analysis and they can be used for the analysis of angiograms and facilitate diagnostics. We have developed an algorithm based on Convolutional Neural Network and Neural Network U-Net [2] for vessels segmentation and defects detection such as stenosis. For our research we used anonymized angiography data obtained from one of the city's hospitals and augmented them to improve learning efficiency. U-Net usage provided high quality segmentation and the combination of our algorithm with an ensemble of classifiers shows a good accuracy in the task of ischemia evaluation on test data. Subsequently, this approach can be served as a basis for the creation of an analytical system that could speed up the diagnosis of cardiovascular diseases and greatly facilitate the work of a specialist.