Corpus ID: 51866556

Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks

  title={Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks},
  author={Benjamin Au and Uri Shaham and S. Dhruva and G. Bouras and Ecaterina Cristea and A. Lansky and A. Coppi and F. Warner and Shu-Xia Li and H. Krumholz},
The determination of a coronary stenosis and its severity in current clinical workflow is typically accomplished manually via physician visual assessment (PVA) during invasive coronary angiography. While PVA has shown large inter-rater variability, the more reliable and accurate alternative of Quantitative Coronary Angiography (QCA) is challenging to perform in real-time due to the busy workflow in cardiac catheterization laboratories. We propose a deep learning approach based on Convolutional… Expand
Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease
Deep learning segmentation of major vessels in X-ray coronary angiography
Artificial Intelligence and Machine Learning in Cardiovascular Healthcare.
  • A. Kilic
  • Medicine
  • The Annals of thoracic surgery
  • 2019


Vessel extraction in X-ray angiograms using deep learning
  • E. Nasr-Esfahani, S. Samavi, +6 authors K. Najarian
  • Computer Science, Medicine
  • 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2016
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
Automatic segmentation of the left ventricle in cardiac CT angiography using convolutional neural networks
Automatic detection of coronary stenosis in X-ray angiography through spatio-temporal tracking
Comparison of Physician Visual Assessment With Quantitative Coronary Angiography in Assessment of Stenosis Severity in China