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Optical Coherence Tomography Angiography (OCT-A) is a non-invasive imagingtechnique, and has been increasingly used to image the retinal vasculature atcapillary level resolution. However, automated segmentation of retinal vesselsin OCT-A has been under-studied due to various challenges such as low capillaryvisibility and high vessel complexity, despite its significance inunderstanding many eye-related diseases. In addition, there is no publiclyavailable OCT-A dataset with manually graded vessels for training andvalidation. To address these issues, for the first time in the field of retinalimage analysis we construct a dedicated Retinal OCT-A SEgmentation dataset(ROSE), which consists of 229 OCT-A images with vessel annotations at eithercenterline-level or pixel level. This dataset has been released for publicaccess to assist researchers in the community in undertaking research inrelated topics. Secondly, we propose a novel Split-based Coarse-to-Fine vesselsegmentation network (SCF-Net), with the ability to detect thick and thinvessels separately. In the SCF-Net, a split-based coarse segmentation (SCS)module is first introduced to produce a preliminary confidence map of vessels,and a split-based refinement (SRN) module is then used to optimize theshape/contour of the retinal microvasculature. Thirdly, we perform a thoroughevaluation of the state-of-the-art vessel segmentation models and our SCF-Neton the proposed ROSE dataset. The experimental results demonstrate that ourSCF-Net yields better vessel segmentation performance in OCT-A than bothtraditional methods and other deep learning methods.

ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model