  The reliable segmentation of retinal vasculature can provide the means to
diagnose and monitor the progression of a variety of diseases affecting the
blood vessel network, including diabetes and hypertension. We leverage the
power of convolutional neural networks to devise a reliable and fully automated
method that can accurately detect, segment, and analyze retinal vessels. In
particular, we propose a novel, fully convolutional deep neural network with an
encoder-decoder architecture that employs dilated spatial pyramid pooling with
multiple dilation rates to recover the lost content in the encoder and add
multiscale contextual information to the decoder. We also propose a simple yet
effective way of quantifying and tracking the widths of retinal vessels through
direct use of the segmentation predictions. Unlike previous deep-learning-based
approaches to retinal vessel segmentation that mainly rely on patch-wise
analysis, our proposed method leverages a whole-image approach during training
and inference, resulting in more efficient training and faster inference
through the access of global content in the image. We have tested our method on
two publicly available datasets, and our state-of-the-art results on both the
DRIVE and CHASE-DB1 datasets attest to the effectiveness of our approach.
