  Estimating clinically-relevant vascular features following vessel
segmentation is a standard pipeline for retinal vessel analysis, which provides
potential ocular biomarkers for both ophthalmic disease and systemic disease.
In this work, we integrate these clinical features into a novel vascular
feature optimised loss function (VAFO-Loss), in order to regularise networks to
produce segmentation maps, with which more accurate vascular features can be
derived. Two common vascular features, vessel density and fractal dimension,
are identified to be sensitive to intra-segment misclassification, which is a
well-recognised problem in multi-class artery/vein segmentation particularly
hindering the estimation of these vascular features. Thus we encode these two
features into VAFO-Loss. We first show that incorporating our end-to-end
VAFO-Loss in standard segmentation networks indeed improves vascular feature
estimation, yielding quantitative improvement in stroke incidence prediction, a
clinical downstream task. We also report a technically interesting finding that
the trained segmentation network, albeit biased by the feature optimised loss
VAFO-Loss, shows statistically significant improvement in segmentation metrics,
compared to those trained with other state-of-the-art segmentation losses.
