Computer Vision News - July 2020

2 Summary RSIP Vision Projects 12 Non-conclusive analysis leads to increased rates of invasive procedures, such as fractional flow reserve (FFR) measurement during ICA, and delayed CAD management. Methods – A two-stage DL method may be used to identify CCTA image voxels that represent coronary artery calcification. The method uses two separate CNNs. The first segments the image and identifies potential calcification, and the second CNN classifies only the voxels selected by the first CNN to identify functional significance. This approach achieves a high rate of accuracy. Results – Deep Learning architectures are more sensitive and quicker in identifying functionally significant stenosis than standard CCTA which despite high negative predictive value in exclusion is less efficient at detecting and identifying CAC and functionally significant stenosis. Low-Dose Contrast Analysis: The usage of contrast agents in coronary computed tomography angiography improves image quality and segmentation accuracy contributing to overall diagnostic efficiency. However, patient safety is compromised by increased dosage. On the other hand, dose reduction increases noise and artifact interference, impacting image interpretation and making classic CV tolls less effective. DL algorithms are enhancing low dose CCTA image quality, thereby lowering dose radiation for contrast CCTA and improving clinical analysis.