  Coronary angiography continues to serve as the primary method for diagnosing
coronary artery disease (CAD), which is the leading global cause of mortality.
The severity of CAD is quantified by the location, degree of narrowing
(stenosis), and number of arteries involved. In current practice, this
quantification is performed manually using visual inspection and thus suffers
from poor inter- and intra-rater reliability. The MICCAI grand challenge:
Automatic Region-based Coronary Artery Disease diagnostics using the X-ray
angiography imagEs (ARCADE) curated a dataset with stenosis annotations, with
the goal of creating an automated stenosis detection algorithm. Using a
combination of machine learning and other computer vision techniques, we
propose the architecture and algorithm StenUNet to accurately detect stenosis
from X-ray Coronary Angiography. Our submission to the ARCADE challenge placed
3rd among all teams. We achieved an F1 score of 0.5348 on the test set, 0.0005
lower than the 2nd place.
