Feature pyramid U-Net for retinal vessel segmentation

Abstract

The retinal vessel is the only microvascular network that can be directly and non-invasively observed in humans. Cardiovascular and cerebrovascular diseases, such as diabetes, hypertension, can lead to structural changes of the retinal microvascular network. Therefore, it is of great significance to study effective retinal vessel segmentation methods and assist doctors in early diagnoses with quantitative results for vascular networks. In this study, we propose a novel convolutional neural network named feature pyramid U-Net (FPU-Net) that extracts multiscale representations by constructing two feature pyramids both on the encoder and the decoder of U-Net. In this representation, objects features with different size like micro-vessels and pathology will be fused for better vessel segmentation. The experimental results show that compared with state-of-the-art methods, FPU-Net is superior in terms of accuracy, sensitivity, F1-score, and area under the curve and capable of stronger domain generalisation across different datasets.