AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography

Abstract Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye flushed through the coronary vessels to visualize severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation approximate percent diameter reduction stenosis, and this directs therapies like stent placement. A fully automatic method segment would eliminate potential subjectivity provide a quantitative systematic measurement reduction. Here, we have designed convolutional neural network, AngioNet, for segmentation angiography images. The main innovation network introduction an Angiographic Processing Network (APN) significantly improves performance on multiple backbones, with best Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). purpose APN create end-to-end pipeline image pre-processing segmentation, learning possible filters improve segmentation. We also demonstrated interchangeability our measuring Quantitative Angiography. Our results indicate that AngioNet powerful tool angiographic could facilitate anatomical assessment stenosis clinical workflow.