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RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification by Morano, José; Aresta, Guilherme; Bogunović, Hrvoje is a scholarly article available to read on EtoBox.

What is RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification about?

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. Evaluatio

Author

Morano, José; Aresta, Guilherme; Bogunović, Hrvoje

Published

2024

Language

EN