  The retina is the only part of the human body in which blood vessels can be
accessed non-invasively using imaging techniques such as digital fundus images
(DFI). The spatial distribution of the retinal microvasculature may change with
cardiovascular diseases and thus the eyes may be regarded as a window to our
hearts. Computerized segmentation of the retinal arterioles and venules (A/V)
is essential for automated microvasculature analysis. Using active learning, we
created a new DFI dataset containing 240 crowd-sourced manual A/V segmentations
performed by fifteen medical students and reviewed by an ophthalmologist, and
developed LUNet, a novel deep learning architecture for high resolution A/V
segmentation. LUNet architecture includes a double dilated convolutional block
that aims to enhance the receptive field of the model and reduce its parameter
count. Furthermore, LUNet has a long tail that operates at high resolution to
refine the segmentation. The custom loss function emphasizes the continuity of
the blood vessels. LUNet is shown to significantly outperform two
state-of-the-art segmentation algorithms on the local test set as well as on
four external test sets simulating distribution shifts across ethnicity,
comorbidities, and annotators. We make the newly created dataset open access
(upon publication).
