  Retinal Vessel Segmentation is important for the diagnosis of various
diseases. The research on retinal vessel segmentation focuses mainly on the
improvement of the segmentation model which is usually based on U-Net
architecture. In our study, we use the U-Net architecture and we rely on heavy
data augmentation in order to achieve better performance. The success of the
data augmentation relies on successfully addressing the problem of input
images. By analyzing input images and performing the augmentation accordingly
we show that the performance of the U-Net model can be increased dramatically.
Results are reported using the most widely used retina dataset, DRIVE.
