摘要：Retinal vasculature provides an opportunity for direct observation of vessel morphology, which is linked to multiple clinical conditions . However, objective and quantitative interpretation of the retinal vasculature relies on precise vessel segmentation, which is time consuming and labor intensive . Artifcial intelligence (AI) has demonstrated great promise in retinal vessel segmentation . The development and evaluation of AI-based models require large numbers of annotated retinal images . However, the public datasets that are usable for this task are scarce . In this paper, we collected a color fundus image vessel segmentation (FIVES) dataset . The FIVES dataset consists of 800 high-resolution multi-disease color fundus photographs with pixelwise manual annotation . The annotation process was standardized through crowdsourcing among medical experts . The quality of each image was also evaluated . To the best of our knowledge, this is the largest retinal vessel segmentation dataset for which we believe this work will be benefcial to the further development of retinal vessel segmentation .
