The retinal vessel segmentation is of great importance in the diagnosis of numerous diseases including diabetic retinopathy, atherosclerosis and hypertension. The structure of the vasculature is characterized by vessels of different thicknesses and lengths, and, given the strong imbalance between the vascular part and the background, this segmentation task is rather complex, especially if the specialist is not supported by Artificial Intelligence algorithms. In this work, the dataset OCTA-500 has been used to perform a supervised segmentation. The portion of the dataset that has been selected contains 2D images from the projection map of volumetric images obtained by Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA), with associated segmentation masks.