# Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks

arXiv: https://arxiv.org/abs/2103.02969 · HTML: https://mlsanity.com/paper/2103.02969/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 4 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Discriminative Correlation Filter with Channel and Spatial Reliability](https://mlsanity.com/paper/1611.08461/) (2016)
- [A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI](https://mlsanity.com/paper/1512.07951/) (2015)
- [Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation](https://mlsanity.com/paper/1608.03974/) (2016)
- [Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks](https://mlsanity.com/paper/1807.10597/) (2018)
- [Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network](https://mlsanity.com/paper/1704.05698/) (2017)

## Built on by (most cited first, 6 of 6)

- [CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method](https://mlsanity.com/paper/2407.19894/) (2024)
- [Neural network-based coronary dominance classification of RCA angiograms](https://mlsanity.com/paper/2309.06958/) (2023)
- [ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis](https://mlsanity.com/paper/2310.04749/) (2023)
- [Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data](https://mlsanity.com/paper/2602.02067/) (2026)
- [Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification](https://mlsanity.com/paper/2606.02605/) (2026)
- [Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation](https://mlsanity.com/paper/2310.05990/) (2023)

## Abstract (arXiv)

Coronary artery disease leading up to stenosis, the partial or total blocking of coronary arteries, is a severe condition that affects millions of patients each year. Automated identification and classification of stenosis severity from minimally invasive procedures would be of great clinical value, but existing methods do not match the accuracy of experienced cardiologists, due to the complexity of the task. Although a number of computational approaches for quantitative assessment of stenosis have been proposed to date, the performance of these methods is still far from the required levels for clinical applications. In this paper, we propose a two-step deep-learning framework to partially automate the detection of stenosis from X-ray coronary angiography images. In the two steps, we used two distinct convolutional neural network architectures, one to automatically identify and classify the angle of view, and another to determine the bounding boxes of the regions of interest in frames where stenosis is visible. Transfer learning and data augmentation techniques were used to boost the performance of the system in both tasks. We achieved a 0.97 accuracy on the task of classifying the Left/Right Coronary Artery (LCA/RCA) angle view and 0.68/0.73 recall on the determination of the regions of interest, for LCA and RCA, respectively. These results compare favorably with previous results obtained using related approaches, and open the way to a fully automated method for the identification of stenosis severity from X-ray angiographies.