Abstract: Retinal vessel segmentation is the basis of the ophthalmic disease computer-aided diagnosis and large-scale screening system. This paper reviews the progress of retinal vessel segmentation in fundus image. Paper outlines the background and significance of this research, the commonly used standard databases, performance metrics, the advantages and disadvantages of the vessel segmentation algorithms. It is aimed at quickly guiding researchers to understand the contents of this field. The method of retinal vessel segmentation can be divided into five main categories: blood vessel tracking, matched filtering, mathematical morphology, deformable model based, and machine learning. All the methods contain their own characteristics and contribute to the latter researches, among which machine learning based method is the most important one. It provides the decision support for the computer-aided diagnosis with clues by data-driven approach. Although researchers have done a lot of work, retinal vessel segmentation still can be improved in accuracy and efficiency. There are many difficulties to be resolved in retinal vessel segmentation, such as the interference by physiological structure and lesions, and the segmentation of microvascular, vessels on the optic disc and intraretinal microvascular abnormalities（IRMA）.