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

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. However, automated segmentation of vessels in OCTA under-studied due various challenges such as low visibility and high vessel complexity, despite its significance understanding many vision-related diseases. In addition, there no publicly available dataset with manually graded for training validation algorithms. To address these issues, first time field analysis we construct dedicated Retinal SEgmentation (ROSE), which consists 229 images annotations either centerline-level or pixel level. This source code released public access assist researchers community undertaking research related topics. Secondly, introduce novel split-based coarse-to-fine network (OCTA-Net), ability detect thick thin separately. OCTA-Net, coarse module utilized produce preliminary confidence map vessels, refined then optimize shape/contour microvasculature. We perform thorough evaluation state-of-the-art models our OCTA-Net on constructed ROSE dataset. The experimental results demonstrate yields better performance than both traditional other deep learning methods. provide fractal dimension segmented microvasculature, statistical demonstrates significant differences between healthy control Alzheimer's Disease group. consolidates microvasculature may offer new scheme study neurodegenerative