The caliber and configuration of retinal blood vessels serve as important
biomarkers for various diseases and medical conditions. A thorough analysis of
the retinal vasculature requires the segmentation of blood vessels and their
classification into arteries and veins, which is typically performed on color
fundus images obtained by retinography, a widely used imaging technique.
Nonetheless, manually performing these tasks is labor-intensive and prone to
human error. Various automated methods have been proposed to address this
problem. However, the current state of art in artery/vein segmentation and
classification faces challenges due to manifest classification errors that
affect the topological consistency of segmentation maps. This study presents an
innovative end-to-end framework, RRWNet, designed to recursively refine
semantic segmentation maps and correct manifest classification errors. The
framework consists of a fully convolutional neural network with a Base
subnetwork that generates base segmentation maps from input images, and a
Recursive Refinement subnetwork that iteratively and recursively improves these
maps. Evaluation on public datasets demonstrates the state-of-the-art
performance of the proposed method, yielding more topologically consistent
segmentation maps with fewer manifest classification errors than existing
approaches. In addition, the Recursive Refinement module proves effective in
post-processing segmentation maps from other methods, automatically correcting
classification errors and improving topological consistency. The model code,
weights, and predictions are publicly available at
this https URL

通过 RRWNet 模型，对视网膜血管进行分割和分类，有效地纠正错误分类，提高拓扑一致性。