  The retinal vascular condition is a reliable biomarker of several
ophthalmologic and cardiovascular diseases, so automatic vessel segmentation
may be crucial to diagnose and monitor them. In this paper, we propose a novel
method that combines the multiscale analysis provided by the Stationary Wavelet
Transform with a multiscale Fully Convolutional Neural Network to cope with the
varying width and direction of the vessel structure in the retina. Our proposal
uses rotation operations as the basis of a joint strategy for both data
augmentation and prediction, which allows us to explore the information learned
during training to refine the segmentation. The method was evaluated on three
publicly available databases, achieving an average accuracy of 0.9576, 0.9694,
and 0.9653, and average area under the ROC curve of 0.9821, 0.9905, and 0.9855
on the DRIVE, STARE, and CHASE_DB1 databases, respectively. It also appears to
be robust to the training set and to the inter-rater variability, which shows
its potential for real-world applications.
