NFN＋: A novel network followed network for retinal vessel segmentation

Abstract

In the early diagnosis of diabetic retinopathy, the morphological attributes of blood vessels play an essential role to construct a retinal computer-aided diagnosis system. However, due to the challenges including limited densely annotated data, inter-vessel differences and structured prediction problem, it remains challenging to segment accurately the retinal vessels, particularly the capillaries on color fundus images. To address these issues, in this paper, we propose a novel deep learning-based model called NFN＋ to effectively extract multi-scale information and make full use of deep feature maps. In NFN＋, the front network converts an image patch into a probabilistic retinal vessel map, and the followed network further refines the map to achieve a better post-processing module, which helps represent the vessel structures implicitly. We employ the inter-network skip connections to unite two identical multi-scale backbones, which enables the useful multi-scale features to be directly transferred from shallow layers to deeper layers. The refined probabilistic retinal vessel maps produced from the augmented images are then averaged to construct the segmentation results. We evaluated this model on the digital retinal images for vessel extraction (DRIVE), structured analysis of the retina (STARE), and the child heart and health study (CHASE) databases. Our results indicate that the elaborated cascaded designs can produce performance gain and the proposed NFN＋ model, to our best knowledge, achieved the state-of-the-art retinal vessel segmentation accuracy on color fundus images (AUC: 98.30%, 98.75% and 98.94%, respectively).

Keywords: Cascaded networks; Deep learning; Retinal vessel segmentation; Skip connections.