  The morphological attributes of retinal vessels, such as length, width,
tortuosity and branching pattern and angles, play an important role in
diagnosis, screening, treatment, and evaluation of various cardiovascular and
ophthalmologic diseases such as diabetes, hypertension and arteriosclerosis.
The crucial step before extracting these morphological characteristics of
retinal vessels from retinal fundus images is vessel segmentation. In this
work, we propose a method for retinal vessel segmentation based on fully
convolutional networks. Thousands of patches are extracted from each retinal
image and then fed into the network, and data argumentation is applied by
rotating extracted patches. Two architectures of fully convolutional networks,
U-Net and LadderNet, are used for vessel segmentation. The performance of our
method is evaluated on three public datasets: DRIVE, STARE, and CHASE\_DB1.
Experimental results of our method show superior performance compared to recent
state-of-the-art methods.
