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:
Wed, 09/23/2020 - 00:44
DOI:
10.21227/m9yw-xs04
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License:
Creative Commons Attribution
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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.

Comments

great!

Submitted by Xiaobo Lai on Fri, 09/25/2020 - 21:56

Scientific research

Submitted by simon ma on Tue, 08/31/2021 - 00:54