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).

中文翻译：

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NFN ＋：一种用于视网膜血管分割的新型网络。

在糖尿病性视网膜病变的早期诊断中，血管的形态学特征对构建视网膜计算机辅助诊断系统起着至关重要的作用。然而，由于挑战，包括有限的密集注释数据，血管间差异和结构化的预测问题，准确地分割视网膜血管，尤其是彩色眼底图像上的毛细血管仍然具有挑战性。为了解决这些问题，在本文中，我们提出了一种新颖的基于深度学习的模型NFN ＋，可以有效地提取多尺度信息并充分利用深度特征图。在NFN ＋中，前部网络将图像斑块转换为概率性视网膜血管图，随后的网络进一步细化该图以实现更好的后处理模块，从而有助于隐式表示血管结构。我们使用网络间跳过连接来联合两个相同的多尺度主干，这使得有用的多尺度特征可以直接从浅层转移到较深层。然后，将从增强图像产生的精炼的概率性视网膜血管图平均，以构建分割结果。我们在数字视网膜图像上评估了该模型，以进行血管提取（DRIVE），视网膜的结构化分析（STARE）和儿童心脏与健康研究（CHASE）数据库。我们的结果表明，精心设计的级联设计可以提高性能，并且据我们所知，所提出的NFN +模型在彩色眼底图像上实现了最新的视网膜血管分割精度（AUC：98.30％，98.75％和98.94） ％， 分别）。