  Coronary angiography is considered to be a safe tool for the evaluation of
coronary artery disease and perform in approximately 12 million patients each
year worldwide. [1] In most cases, angiograms are manually analyzed by a
cardiologist. Actually, there are no clinical practice algorithms which could
improve and automate this work. Neural networks show high efficiency in tasks
of image analysis and they can be used for the analysis of angiograms and
facilitate diagnostics. We have developed an algorithm based on Convolutional
Neural Network and Neural Network U-Net [2] for vessels segmentation and
defects detection such as stenosis. For our research we used anonymized
angiography data obtained from one of the city's hospitals and augmented them
to improve learning efficiency. U-Net usage provided high quality segmentation
and the combination of our algorithm with an ensemble of classifiers shows a
good accuracy in the task of ischemia evaluation on test data. Subsequently,
this approach can be served as a basis for the creation of an analytical system
that could speed up the diagnosis of cardiovascular diseases and greatly
facilitate the work of a specialist.
