  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 the blood vessels and
their classification into arteries and veins, typically performed on color
fundus images obtained by retinography. However, manually performing these
tasks is labor-intensive and prone to human error. While several automated
methods have been proposed to address this task, the current state of art faces
challenges due to manifest classification errors affecting the topological
consistency of segmentation maps. In this work, we introduce RRWNet, a novel
end-to-end deep learning framework that addresses this limitation. The
framework consists of a fully convolutional neural network that recursively
refines semantic segmentation maps, correcting manifest classification errors
and thus improving topological consistency. In particular, RRWNet is composed
of two specialized subnetworks: a Base subnetwork that generates base
segmentation maps from the input images, and a Recursive Refinement subnetwork
that iteratively and recursively improves these maps. Evaluation on three
different 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 within RRWNet proves effective in post-processing
segmentation maps from other methods, further demonstrating its potential. The
model code, weights, and predictions will be publicly available at
https://github.com/j-morano/rrwnet.
