  The retinal vasculature provides important clues in the diagnosis and
monitoring of systemic diseases including hypertension and diabetes. The
microvascular system is of primary involvement in such conditions, and the
retina is the only anatomical site where the microvasculature can be directly
observed. The objective assessment of retinal vessels has long been considered
a surrogate biomarker for systemic vascular diseases, and with recent
advancements in retinal imaging and computer vision technologies, this topic
has become the subject of renewed attention. In this paper, we present a novel
dataset, dubbed RAVIR, for the semantic segmentation of Retinal Arteries and
Veins in Infrared Reflectance (IR) imaging. It enables the creation of deep
learning-based models that distinguish extracted vessel type without extensive
post-processing. We propose a novel deep learning-based methodology, denoted as
SegRAVIR, for the semantic segmentation of retinal arteries and veins and the
quantitative measurement of the widths of segmented vessels. Our extensive
experiments validate the effectiveness of SegRAVIR and demonstrate its superior
performance in comparison to state-of-the-art models. Additionally, we propose
a knowledge distillation framework for the domain adaptation of RAVIR
pretrained networks on color images. We demonstrate that our pretraining
procedure yields new state-of-the-art benchmarks on the DRIVE, STARE, and
CHASE_DB1 datasets. Dataset link: https://ravirdataset.github.io/data/
