Image Quality Estimation
The purpose of this challenge is to evaluate fundus image quality
Recently, automatic DR diagnosis has gained significant research and clinical interests, while the image quality plays a key role in the diagnosis accuracy. Even though the technology used in digital fundus imaging has improved, non-biological factors resulting from the improper operation can still reduce image quality. The major factors in fundus image quality assessment are image artifact, clarity, and field definition as shown in Figure.

Figure. Images (a)-(c) are ungradable for DR due to image artifacts (a), clarity (b), and field definition (c), respectively. Image (d) is a gradable image.
Table. Image Quality Scoring Criteria
Type | Image quality specification | level | ||||||||||||
Overall quality |
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Artifact |
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Clarity |
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Field definition |
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Fundus quality assessment. The sub challenge can be divided in four different tasks; participants can submit results for at least one of the following tasks:
a) Overall image quality
b) Artifact
c) Clarity
d) Field definition
Initially 60% of the database with ground truth (i.e. Training set with labels) will be released on December 1, 2019 (four months before the date of the symposium). The validation set 20% will be provided on January 15, 2020 (two and half months before the day of on-site competition) and 20% (test set without labels) of remaining data will be released on the day of challenge. The released data will be divided proportionately per grade. The test set results will be used for comparison and analysis of submitted results.
For image quality estimation (sub-challenge2), this challenge evaluates the performance of the algorithms for image classification accuracy using the available grades from the experts.
Submissions of results will be accepted in .csv file with header three headers as Test Image No. – Quality Grade.
This challenge evaluates the performance of the algorithms for image classification accuracy using the available grades from the experts.