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

Abstract

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. This paper proposes a novel Fully Convolutional Neural Network architecture capable of processing high resolution images through a large receptive field at a minimal memory and computational cost. First, a single branch CNN is trained on whole images at low resolution to learn large scale features. Then, this branch is incorporated into a standard encoder/decoder FCNN: its large scale features are concatenated to those computed by the central layer of the FCNN. Finally, the whole network architecture is trained on high-resolution patches. During this last phase, the FCNN benefits from the large scale features while the low resolution branch parameters are fine-tuned. This architecture was evaluated on the publicly available retinal fundus database DRIVE. The trained network achieves an accuracy of 96.1% in segmenting the full retinal vessels and improves by 5% the artery/vein classification compared to a basic U-Net.