The project web for "Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces" in MICCAI 2019.
Eye-Quality (EyeQ) Assessment Dataset is a re-annotatation subset from the EyePACS dataset for fundus image quality assessment.
EyeQ dataset has 28,792 retinal images with a three-level quality grading (i.e., 'Good', 'Usable' and 'Reject').
| Train | - | - | - | - | - | Test | - | - | - | - | - | Total | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DR-0 | DR-1 | DR-2 | DR-3 | DR-4 | All | DR-0 | DR-1 | DR-2 | DR-3 | DR-4 | All | ||
| Good | 6,342 | 699 | 1,100 | 167 | 39 | 8,347 | 5,966 | 886 | 1,354 | 199 | 65 | 8,470 | 16,817 |
| Usable | 1,353 | 103 | 283 | 79 | 58 | 1,896 | 3,201 | 359 | 721 | 145 | 133 | 4,559 | 6,435 |
| Reject | 1,544 | 109 | 426 | 87 | 154 | 2,320 | 2,195 | 153 | 569 | 104 | 199 | 3,220 | 5,540 |
| Total | 9,239 | 911 | 1,809 | 333 | 251 | 12,543 | 1,1362 | 1,398 | 2,644 | 448 | 397 | 16,249 | 28,792 |
- The original fundus images could be downloaded from EyePACS dataset.
- All the original fundus images should be pre-porcessed by 'EyeQ_process_main.py' in folder 'EyeQ_preprocess'.
- The quality label is in 'Label_EyeQ_v1.xlsx', where the 'train' and 'test' list is divided by EyePACS, and the 'DR_grade' label is also from EyePACS.
If you use this dataset and code, please cite the following papers:
[1] Huazhu Fu, Boyang Wang, Jianbing Shen, Shanshan Cui, Yanwu Xu, Jiang Liu, Ling Shao, "Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces", in MICCAI, 2019. [PDF]
Update log:
- 19.07.10: Released the dataset.