RECOVERY-FA19: Ultra-Widefield Fluorescein Angiography Vessel Detection Dataset
- Citation Author(s):
-
Li Ding
(University of Rochester)
Mohammad H. Bawany
(University of Rochester)
Ajay E. Kuriyan (University of Rochester)Rajeev S. Ramchandran (University of Rochester)Charles C. Wykoff (Houston Methodist Hospital)Gaurav Sharma
(University of Rochester)
- Submitted by:
- Li Ding
- Last updated:
- DOI:
- 10.21227/m9yw-xs04
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- Research Article Link:
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Abstract
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.
Instructions:
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.
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