Corpus ID: 51866556

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

@article{Au2018AutomatedCO,
  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},
  journal={ArXiv},
  year={2018},
  volume={abs/1807.10597}
}
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
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