Automated Assessment of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks

Several methods for quantitative severity assessment of coronary artery stenosis exist as well as different measures, leading to distinct management of treatment procedures. It is of upmost importance to properly identify and classify all possible stenosis on an individual. A deep-learning three-step framework implementation was designed to automate the detection and assessment of stenosis severity. This study showcases a new clinically obtained dataset of properly de-identified X-ray invasive coronary angiography (ICA) sequences of 438 patients from Hospital de Santa Maria. For each sequence, radio-opaque contrast filled frames were annotated, defining full stenosis visibility with stenosis bounding boxes being annotated by an expert physician on reference frames followed by image processing techniques for propagation at each frame. Transfer learning dynamics of deep neural networks are exploited for supervised learning at each step, employing CNN's for angle view selection of the Left/Right Coronary Artery (LCA/RCA) achieving 0.97 Accuracy, single-shot detectors for stenosis detection achieving 0.83/0.81 mAR for LCA/RCA respectively and a new region of interest boost approach with CNN's for stenosis severity regression of the RCA was explored. Our method showcases the importance of transfer learning in stenosis severity assessment with limited data, achieving considerable performances. To the best of the author's knowledge, this is the first time that iFR was used as a metric for stenosis severity assessment tasks using deep learning techniques.