Segmentation boosting with compensation methods in optical coherence tomography angiography images

Optical coherence tomography angiography is a noninvasive imaging modality to establish the diagnosis of retinal vascular diseases. However, angiography images are significantly interfered if patients jitter or blink. In this study, a novel retinal image analysis method to accurately detect blood vessels and compensate the effect of interference was proposed. We call this the patch U-Net compensation (PUC) system, which is based on the famous U-Net. Several techniques, including a better training mechanism, direction criteria, area criteria, gap criteria, and probability map criteria, have been proposed to improve its accuracy. Simulations show that the proposed PUC achieves much better performance than state-of-art methods.