A supervised method using convolutional neural networks for retinal vessel delineation

Abstract

Retinal vessel delineation is a hot research topic owing to its importance in a lot of clinic application. Several methods have been proposed in the past decades. Here we will present a new supervised method for retinal vessel segmentation. The method is designed to explore the complex relationship between retinal images and their corresponding vessel label maps. Specifically, in order to build a model describing the direct transformation from retinal image to vessel map, we introduce a deep convolutional neural network (abbreviation as CNN), which has strong enough induction ability. For the purpose of constructing the whole vessel probability map, we also design a synthesis method. Our method shows better performance on DRIVE dataset than state-of-the-art of reported approaches in the light of sensitivity (abbreviation as Se), specificity (abbreviation as Sp) and accuracy (abbreviation as Acc). Our proposed method has great potential to be applied in existing computer-assisted diagnostic system of ophthalmologic diseases. Meanwhile, the method may offer a novel, general computing framework for segmentation in other fields.