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

ABSTRACT: Vasculature morphology and hierarchy are essential for blood perfusion. Human retinal circulation is an intricate vascular system emerging and remerging at the optic nerve head (ONH). Tracing retinal vascular branching from ONH can allow detailed morphological quantification, and yet remains a challenging task. We presented a robust semi-automatic vessel tracing algorithm on human fundus images by an instance segmentation neural network (InSegNN). InSegNN separates and labels individual vascular trees and enables tracing each tree throughout its branching. We have three strategies to improve robustness and accuracy: pseudotemporal learning, spatial multisampling, and dynamic probability map. We achieved 83% specificity, 50% improvement in symmetric best dice (SBD) compared to literature, a

SUBMITTER: Chen S 

PROVIDER: S-EPMC11970458 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature