  The morphology and hierarchy of the vascular systems are essential for
perfusion in supporting metabolism. In human retina, one of the most
energy-demanding organs, retinal circulation nourishes the entire inner retina
by an intricate vasculature emerging and remerging at the optic nerve head
(ONH). Thus, tracing the vascular branching from ONH through the vascular tree
can illustrate vascular hierarchy and allow detailed morphological
quantification, and yet remains a challenging task. Here, we presented a novel
approach for a robust semi-automatic vessel tracing algorithm on human fundus
images by an instance segmentation neural network (InSegNN). Distinct from
semantic segmentation, InSegNN separates and labels different vascular trees
individually and therefore enable tracing each tree throughout its branching.
We have built-in three strategies to improve robustness and accuracy with
temporal learning, spatial multi-sampling, and dynamic probability map. We
achieved 83% specificity, and 50% improvement in Symmetric Best Dice (SBD)
compared to literature, and outperformed baseline U-net. We have demonstrated
tracing individual vessel trees from fundus images, and simultaneously retain
the vessel hierarchy information. InSegNN paves a way for any subsequent
morphological analysis of vascular morphology in relation to retinal diseases.
