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 https://github.com/j-morano/rrwnet.

翻译：视网膜血管的管径与构型是多种疾病和医学状态的重要生物标志物。对视网膜血管系统的全面分析需要对血管进行分割，并将其分类为动脉和静脉，这一过程通常基于由视网膜成像（一种广泛使用的成像技术）获取的彩色眼底图像进行。然而，人工执行这些任务不仅耗时且易受人为误差影响。尽管已有多种自动化方法被提出以解决此问题，但当前动脉/静脉分割与分类的最先进技术仍面临显著分类错误影响分割图拓扑一致性的挑战。本研究提出了一种创新的端到端框架RRWNet，旨在递归精化语义分割图并纠正显著分类错误。该框架由全卷积神经网络构成，包含一个基础子网络（用于从输入图像生成基础分割图）和一个递归精化子网络（通过迭代递归方式逐步优化上述分割图）。在公开数据集上的评估表明，所提方法达到了最先进性能，生成的拓扑一致性分割图中显著分类错误数量少于现有方法。此外，递归精化模块能有效后处理来自其他方法的分割图，自动纠正分类错误并提升拓扑一致性。模型代码、权重及预测结果已公开于https://github.com/j-morano/rrwnet。