Retinal Vessel Segmentation

The Digital Retinal Images for Vessel Extraction (DRIVE) dataset is a dataset for retinal vessel segmentation. It consists of a total of JPEG 40 color fundus images; including 7 abnormal pathology cases. The images were obtained from a diabetic retinopathy screening program in the Netherlands. The images were acquired using Canon CR5 non-mydriatic 3CCD camera with FOV equals to 45 degrees. Each image resolution is 584*565 pixels with eight bits per color channel (3 channels). The set of 40 images was equally divided into 20 images for the training set and 20 images for the testing set. Inside both sets, for each image, there is circular field of view (FOV) mask of diameter that is approximately 540 pixels. Inside training set, for each image, one manual segmentation by an ophthalmological expert has been applied. Inside testing set, for each image, two manual segmentations have been applied by two different observers, where the first observer segmentation is accepted as the ground-truth for performance evaluation. Source: Ant Colony based Feature Selection Heuristics for Retinal Vessel Segmentation Image Source: https://drive.grand-challenge.org/

The STARE (Structured Analysis of the Retina) dataset is a dataset for retinal vessel segmentation. It contains 20 equal-sized (700×605) color fundus images. For each image, two groups of annotations are provided.. Source: DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels Image Source: https://www.researchgate.net/figure/Results-of-the-different-methods-applied-to-the-STARE-dataset-a-original-image-b_fig4_279215756

CHASE_DB1 is a dataset for retinal vessel segmentation which contains 28 color retina images with the size of 999×960 pixels which are collected from both left and right eyes of 14 school children. Each image is annotated by two independent human experts. Source: MixModule: Mixed CNN Kernel Module for Medical Image Segmentation Image Source: https://www.mdpi.com/2073-8994/9/11/276

The HRF dataset is a dataset for retinal vessel segmentation which comprises 45 images and is organized as 15 subsets. Each subset contains one healthy fundus image, one image of patient with diabetic retinopathy and one glaucoma image. The image sizes are 3,304 x 2,336, with a training/testing image split of 22/23. Source: Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation Image Source: https://www.researchgate.net/figure/Examples-of-fundus-images-from-HRF-database-with-corresponding-hand-labelled-gold_fig1_260625531

Retinal OCTA SEgmentation dataset (ROSE) consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level. Source: ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model Image Source: https://imed.nimte.ac.cn/dataofrose.html

The Leuven-Haifa dataset contains 240 disc-centered fundus images of 224 unique patients (75 patients with normal tension glaucoma, 63 patients with high tension glaucoma, 30 patients with other eye diseases and 56 healthy controls) from the University Hospitals of Leuven. The arterioles and venules of these images were both annotated by master students in medicine and corrected by a senior annotator. All senior segmentation corrections are provided as well as the junior segmentations of the test set. An open-source toolbox for the parametrization of segmentations was developed. Diagnosis, age, sex, vascular parameters as well as a quality score are provided as metadata. Potential reuse is envisioned as the development or external validation of blood vessels segmentation algorithms or study of the vasculature in glaucoma and the development of glaucoma diagnosis algorithms. The dataset is available on the KU Leuven Research Data Repository (RDR).

This dataset contains 65 DFIs acquired from patients with POAG at the University of Iowa Hospitals and Clinics. DFIs were acquired using a 30° Zeiss fundus camera (Niemeijer et al 2011). The images were centered on the optic disc. The original DFIs resolution was 2392 × 2048. In order to benchmark LUNet on this dataset, the black border of the DFIs were padded to a squared resolution of 2048 × 2048 pixels and then resized to a 1444 × 1444 pixels resolution. From the resulting DFIs, 15 optic disc-centered DFIs were randomly selected to form the second external test set. No other additional metadata were provided in the open source dataset.

This data set comprises 22 fundus images with their corresponding manual annotations for the blood vessels, separated as arteries and veins. It also include labels for glaucomatous / healthy, differentiating between normal tension glaucoma (NAG) and primary open angle glaucoma (POAG). Further information about the data set is provided in our MICCAI 2018 paper: Orlando, J. I., Breda, J. B., van Keer, K., Blaschko, M. B., Blanco, P. J., & Bulant, C. A. (2018). Towards a glaucoma risk index based on simulated hemodynamics from fundus images. MICCAI 2018. Please, cite the corresponding paper in case you use this data on a scientific publication. Any commercial usage is forbidden. This data set can only be used for scientific purposes.

Standardized Multi-Channel Dataset for Glaucoma (SMDG-19) is a collection and standardization of 19 public datasets, comprised of full-fundus glaucoma images, associated image metadata like, optic disc segmentation, optic cup segmentation, blood vessel segmentation, and any provided per-instance text metadata like sex and age. This dataset is the largest public repository of fundus images with glaucoma.