  Retinal vessel information is helpful in retinal disease screening and
diagnosis. Retinal vessel segmentation provides useful information about
vessels and can be used by physicians during intraocular surgery and retinal
diagnostic operations. Convolutional neural networks (CNNs) are powerful tools
for classification and segmentation of medical images. Complexity of CNNs makes
it difficult to implement them in portable devices such as binocular indirect
ophthalmoscopes. In this paper a simplification approach is proposed for CNNs
based on combination of quantization and pruning. Fully connected layers are
quantized and convolutional layers are pruned to have a simple and efficient
network structure. Experiments on images of the STARE dataset show that our
simplified network is able to segment retinal vessels with acceptable accuracy
and low complexity.
