Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network

Vasculature morphology and hierarchy are essential for blood perfusion.
Human retinal 20 circulation is an intricate vascular system emerging and
remerging at the optic nerve head 21 (ONH). Tracing retinal vascular
branching from ONH can allow detailed morphological 22 quantification, and
yet remains a challenging task. We presented a robust semi-automatic 23
vessel tracing algorithm on human fundus images by an instance
segmentation neural 24 network (InSegNN). InSegNN separates and labels
individual vascular trees and enables 25 tracing each tree throughout its
branching. We have three strategies to improve robustness 26 and accuracy:
pseudo-temporal learning, spatial multi-sampling, and dynamic probability
27 map. We achieved 83% specificity, 50% improvement in Symmetric Best
Dice (SBD) 28 compared to literature, and outperformed baseline U-net, and
achieved 91% precision with 29 71% sensitivity. We have demonstrated
tracing individual vessel trees from fundus 30 images, and simultaneously
retain vessel hierarchy information. InSegNN paves a way for 31 subsequent
analysis of vascular morphology in relation to retinal diseases.