  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.
