Computer Vision News - February 2022

32 AI Research in cardiology Methodology The methodology proposed in this paper is a full end-to-end pipeline which takes as input videos of SE images fromaDICOMfile. These are first classified intoone of 8 view classes (A2C, A3C, A4C & SAX, contrast & non-contrast) by a bespoke CNN built using 10 convolutional layers for view classification. The view classification determines which of the three auto-contouring models must be applied to the respective video clips. ThreeU-net based CNN segmentation frameworks are developed to contour the left ventricle (LV) endocardium in A2C, A4C and SAX views using contrast enhanced and non-contrast studies, and their performance is assessed using Sørensen-Dice Coefficient (DC). The LV contours are then passed to a cardiac cycle selection classifier to algorithmically select end-diastolic and end-systolic contours within a respective cardiac cycle, and R-wave triggers. Here, various geometric features and clinical measurements are computed ( feature calculations ) and input as features for the machine learning ensemble model used for the prediction of CAD. Among these, both routine clinical measures and novel specifically engineered features were developed. The CNNs produced contours that can track the endocardial walls smoothly through time, as it can be observed in parts B and C of the figure above. As shown there, this method well generalizes to the different types of images, including contrast- enhanced images. The whole pipeline, which includes the LV contouring and the extraction of features, is a fundamental step which leads to the novel contributions of this paper. The first one answers the question: How to automatically process SEs and classify whether the patient is likely to have severe coronary disease?