  Retinal imaging serves as a valuable tool for diagnosis of various diseases.
However, reading retinal images is a difficult and time-consuming task even for
experienced specialists. The fundamental step towards automated retinal image
analysis is vessel segmentation and artery/vein classification, which provide
various information on potential disorders. To improve the performance of the
existing automated methods for retinal image analysis, we propose a two-step
vessel classification. We adopt a UNet-based model, SeqNet, to accurately
segment vessels from the background and make prediction on the vessel type. Our
model does segmentation and classification sequentially, which alleviates the
problem of label distribution bias and facilitates training. To further refine
classification results, we post-process them considering the structural
information among vessels to propagate highly confident prediction to
surrounding vessels. Our experiments show that our method improves AUC to 0.98
for segmentation and the accuracy to 0.92 in classification over DRIVE dataset.
