  Optical Coherence Tomography Angiography (OCT-A) is a non-invasive imaging
technique, and has been increasingly used to image the retinal vasculature at
capillary level resolution. However, automated segmentation of retinal vessels
in OCT-A has been under-studied due to various challenges such as low capillary
visibility and high vessel complexity, despite its significance in
understanding many eye-related diseases. In addition, there is no publicly
available OCT-A dataset with manually graded vessels for training and
validation. To address these issues, for the first time in the field of retinal
image analysis we construct a dedicated Retinal OCT-A SEgmentation dataset
(ROSE), which consists of 229 OCT-A images with vessel annotations at either
centerline-level or pixel level. This dataset has been released for public
access to assist researchers in the community in undertaking research in
related topics. Secondly, we propose a novel Split-based Coarse-to-Fine vessel
segmentation network (SCF-Net), with the ability to detect thick and thin
vessels 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 the
shape/contour of the retinal microvasculature. Thirdly, we perform a thorough
evaluation of the state-of-the-art vessel segmentation models and our SCF-Net
on the proposed ROSE dataset. The experimental results demonstrate that our
SCF-Net yields better vessel segmentation performance in OCT-A than both
traditional methods and other deep learning methods.
