Retina blood vessel segmentation using a U-net based Convolutional neural network

This paper applies deep learning techniques to the retinal blood vessels segmentations based on spectral fundus images. It presents a network and training strategy that relies on the data augmentation to use the available annotated samples more efficiently. Thus, the shape, size, and arteriovenous crossing types can be used to get the evidence about the numerous eye diseases. In addition, we apply deep learning based on U-Net convolutional network for real patients’ fundus images. As a result of this, we achieve high performance and its results are much better than the manual way of a skilled ophthalmologist.