The retinal vasculature reveals numerous health conditions, making the quantitative assessment of changes in retinal arteries and veins crucial for disease prevention and management. Quantifying changes in the retinal vasculature requires segmentation to delineate it. Deep-learning techniques demonstrate impressive results for retinal vasculature segmentation in color fundus images. However, even if the generated segmentations are good at the pixel level, they are not coherent at the structural level, (i.e. not anatomically coherent compared to a real retinal vasculature). The vasculature of the retina is composed of two completely connected trees: arteries and veins, whereas segmentations produce several disconnected components. In this article, we propose VNR-AV: a Vasculature Network Retrieval method specifically designed for retinal Arteries and Veins segmentation. The proposed post-processing method achieves two main objectives: it leverages vessels segmentation to enhance the segmentation of arteries and veins by performing reconnection, removal, and detail gathering; and it removes or reconnects segmentation components based on a set of rules developed through an understanding of deep-learning-generated segmentations. VNR-AV retrieve a fully connected thus more anatomically coherent structure of the retinal arteries and veins networks while managing to slightly improve the superposition quality at pixel-level. VNR-AV enable a more coherent assessment of changes in retinal arteries and veins and pave the way for further research in prevention and management of eye-related diseases.Read less <