ROSE: A Retinal OCT-Angiography Vessel SEgmentation Dataset
This dataset is released for academic research use only.
Retinal OCT-Angiography Vessel Segmentation Dataset (ROSE) is now open source. It includes two subsets: ROSE-1 and ROSE-2.
ROSE-1 subset consists of a total of 117 OCTA images from 39 subjects (26 with disease and the rest are healthy control) and was split into 90 images for training and 27 images for testing. Each subject has en face angiograms of superficial (SVC), deep (DVC), and the inner retinal vascular plexus that includes both SVC and DVC (SVC+DVC) respectively. All the OCTA scans were captured by the RTVue XR Avanti SD-OCT system (Optovue, USA) equipped with AngioVue software, with image resolution of 304 × 304 pixels. The scan area was 3×3 mm2 area centred at the fovea. The manual annotations of these vascular network were graded by image experts and clinicians, and the consensus of them was then used as the groundtruth. Two different types of vessel annotations are available: centerline-level and pixel-level annotation.
ROSE-2 subset contains a total of 112 OCT-A images of 112 eyes acquired by Heidelberg OCT2 system with Spectralis software (Heidelberg Engineering, Heidelberg, Germany) and was split into 90 images for training and 22 images for testing. All the images in this dataset are the en face angiograms of SVC within a 3×3 mm2 area centred at the fovea, and each image was resized into a grayscale image with 840 × 840 pixels. All the visible retinal vessels were manually traced using an in-house program written in Matlab (Mathworks R2018, Natwick) by an experienced clinical ophthalmologist.
Examples of manual annotations from ROSE-1 subset
Sample of manual annotation from ROSE-2
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The code of our vessel segmentation model (OCTA-Net) can be found here:
In addition, this package includes several pre-trained SOTA segmentation models and evaluation platform.