  We identify and address three research gaps in the field of vessel
segmentation for funduscopy. The first focuses on the task of inference on
high-resolution fundus images for which only a limited set of ground-truth data
is publicly available. Notably, we highlight that simple rescaling and padding
or cropping of lower resolution datasets is surprisingly effective.
Additionally we explore the effectiveness of semi-supervised learning for
better domain adaptation. Our results show competitive performance on a set of
common public retinal vessel datasets using a small and light-weight neural
network. For HRF, the only very high-resolution dataset currently available, we
reach new state-of-the-art performance by solely relying on training images
from lower-resolution datasets. The second topic concerns evaluation metrics.
We investigate the variability of the F1-score on the existing datasets and
report results for recent SOTA architectures. Our evaluation show that most
SOTA results are actually comparable to each other in performance. Last, we
address the issue of reproducibility by open-sourcing our complete pipeline.
