Segment-wise Evaluation in X-ray Angiography Stenosis Detection

X-ray coronary angiography is the gold standard imaging modality for the assessment of coronary artery disease (CAD). The SYNTAX score is a recommended instrument for therapy decision-making and predicts the postprocedural risk associated with the two revascularization strategies: percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG). The score requires expert assessment and manual measurements of coronary angiograms for stenosis characterization. In this work we propose a deep learning workflow for automated stenosis detection to facilitate the calculation of the SYNTAX score. We use a region-based convolutional neural network for object detection, fine-tuned on a public dataset consisting of angiography frames with annotated stenotic regions. The model is evaluated on angiographic video sequences of complex CAD patients from the German Heart Center of the Charite University Hospital (DHZC), Berlin. We provide a customized graphical tool for cardiac experts that allows correction and segment annotation of the detected stenotic regions. The model reached a precision of 78.39% in the frame-wise object detection task on the clinical dataset. For the task of predicting the presence of coronary stenoses at the patient level, the model achieved a sensitivity of 49.55% for stenoses of all degrees and 59.18% for stenoses of relevant degrees (>75%). The results suggest that our stenosis detection tool can facilitate visual assessment of CAD in angiography data and encourage to investigate further development towards fully automated calculation of the SYNTAX score.