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Datasets & Competitions

The FLoRI21 dataset provides ultra-widefield fluorescein angiography images for the development and evaluation of retinal image registration algorithms. 


Currently, a sample pair of low resolution images is provided and the associated paper is submitted for review. The entire dataset will be released with the publication of the paper.


The PRIME-FP20 dataset is established for development and evaluation of retinal vessel segmentation algorithms in ultra-widefield (UWF) fundus photography (FP). PRIME-FP20 provides 15 high-resolution UWF FP images acquired using the Optos 200Tx camera (Optos plc, Dunfermline, United Kingdom), the corresponding labeled binary vessel maps, and the corresponding binary masks for the valid data region for the images. For each UWF FP image, a concurrently captured UWF fluorescein angiography (FA) is also included. 


UWF FP images, UWF FA images, labeled UWF FP vessel maps, and binary UWF FP validity masks are provided, where the file names indicate the correspondence among them.


Users of the dataset should cite the following paper

L. Ding, A. E. Kuriyan, R. S. Ramchandran, C. C. Wykoff, and G. Sharma, ``Weakly-supervised vessel detection in ultra-widefield fundus photography via iterative multi-modal registration and learning,'' IEEE Trans. Medical Imaging, accepted for publication, to appear.



RECOVERY-FA19 dataset is established for development and evaluation of retinal vessel detection algorithms in fluorescein angiography (FA). RECOVERY-FA19 provides 8 high-resolution ultra-widefield FA images acquired using Optos California P200DTx camera and corresponding labeled binary vessel maps.


Ultra-widefield fluorescein angiography images and corresponding labeled vessel maps are provided where the file names indicate the correspondence between them.

The vessel ground-truth labeling for the RECOVERY-FA19 dataset was performed using the methodology proposed in: 

L. Ding, M. H. Bawany, A. E. Kuriyan, R. S. Ramchandran, C. C. Wykoff, and G. Sharma, ``A novel deep learning pipeline for retinal vessel detection in fluorescein angiography,'' IEEE Trans. Image Proc., vol. 29, no. 1, pp. 6561–6573, 2020. 

Code for evaluating vessel segmentation and replicating results from the above paper can be found in the CodeOcean capsule referenced in the paper. Users of the dataset, should cite the above paper.


We proposed a new dataset, HazeRD, for benchmarking dehazing algorithms under realistic haze conditions. As opposed to prior datasets that made use of synthetically generated images or indoor images with unrealistic parameters for haze simulation, our outdoor dataset allows for more realistic simulation of haze with parameters that are physically realistic and justified by scattering theory. 


I) Installation:Unzip the source code archive. This will create a sub-directory "HazeRD", which is intended to be the directory where you run the MATLAB script. II(a) HazeRD Dataset Generation:Run script demo_simu_haze.m to generate the HazeRD datasetII(b) Computing fidelity metrics for dehazed images with respect to originals:Run script demo_metrics.m to compute the fidelity metrics for dehazed images.Please see the README.txt for detailed instructions.