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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.","url_abs":"https://arxiv.org/abs/2007.05201v2","url_pdf":"https://arxiv.org/pdf/2007.05201v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"rose-a-retinal-oct-angiography-vessel","repo_url":"https://github.com/iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"retinal-vessel-segmentation","task_name":"Retinal Vessel Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"rose","name":"ROSE","full_name":"Retinal OCTA SEgmentation dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/retinal-vessel-segmentation-on-rose-1-dvc","task":"Retinal Vessel Segmentation","dataset":"ROSE-1 DVC","model":"OCTA-Net","rank_in_archive_order":1,"of":5,"metrics":{"Dice Score":"70.74"},"uses_additional_data":false},{"leaderboard":"/sota/retinal-vessel-segmentation-on-rose-1-svc","task":"Retinal Vessel Segmentation","dataset":"ROSE-1 SVC","model":"OCTA-Net","rank_in_archive_order":2,"of":5,"metrics":{"Dice Score":"76.97"},"uses_additional_data":false},{"leaderboard":"/sota/retinal-vessel-segmentation-on-rose-1-svc-dvc","task":"Retinal Vessel Segmentation","dataset":"ROSE-1 SVC-DVC","model":"OCTA-Net","rank_in_archive_order":2,"of":5,"metrics":{"Dice Score":"75.76"},"uses_additional_data":false},{"leaderboard":"/sota/retinal-vessel-segmentation-on-rose-2","task":"Retinal Vessel Segmentation","dataset":"ROSE-2","model":"OCTA-Net","rank_in_archive_order":2,"of":5,"metrics":{"Dice Score":"70.77"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2007.05201","atlas_url":"https://app.syntology.ai/?focus=2007.05201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05201"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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