Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images

Analysis of the retinal vasculature morphology from fundus images, using measures such as arterio-venous ratio, is a promising lead for the early diagnosis of cardiovascular risks. The accuracy of these measures relies on the robustness of the vessels segmentation and classification. However, algorithms based on prior topological knowledge have difficulty modelling the abnormal structure of pathological vasculatures, while patch-trained Fully Convolutional Neural Networks (FCNNs) struggle to learn the wide and extensive topology of the vessels because of their narrow receptive fields.