  Retinal artery/vein (A/V) classification lays the foundation for the
quantitative analysis of retinal vessels, which is associated with potential
risks of various cardiovascular and cerebral diseases. The topological
connection relationship, which has been proved effective in improving the A/V
classification performance for the conventional graph based method, has not
been exploited by the deep learning based method. In this paper, we propose a
Topology Ranking Generative Adversarial Network (TR-GAN) to improve the
topology connectivity of the segmented arteries and veins, and further to boost
the A/V classification performance. A topology ranking discriminator based on
ordinal regression is proposed to rank the topological connectivity level of
the ground-truth, the generated A/V mask and the intentionally shuffled mask.
The ranking loss is further back-propagated to the generator to generate better
connected A/V masks. In addition, a topology preserving module with triplet
loss is also proposed to extract the high-level topological features and
further to narrow the feature distance between the predicted A/V mask and the
ground-truth. The proposed framework effectively increases the topological
connectivity of the predicted A/V masks and achieves state-of-the-art A/V
classification performance on the publicly available AV-DRIVE dataset.
