Abstract
Fundus images are usually used for the diagnosis of ocular pathologies such as diabetic retinopathy. Image quality need however to be sufficient in order to enable grading of the severity of the condition. In this paper, we propose a new method to evaluate the quality of retinal images by computing a score for each image. Images are classified as gradable or ungradable based on this score. First, we use two different U-Net models to segment the macula and the vessels in the original image. We then extract a patch around the macula in the image containing the vessels. Finally, we compute a quality score based on the presence of small vessels in this patch. The score is interpretable as the method is heavily inspired by the way clinicians assess image quality, according to the Scottish Diabetic Retinopathy Grading Scheme. The performances are evaluated on a validation database labeled by a clinician. This method presented a sensitivity of 95% and a specificity of 100% on this database.
Keywords
- Diabetic retinopathy
- Image quality
- Deep learning
- Structure-based
- Data-driven
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Acknowledgements
The authors wish to acknowledge the financial support from the CIHR SPOR Network in Diabetes and its Related Complications (DAC) and the department of ophthalmology at the university of Montreal, Quebec, Canada.
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Peskine, Y., Boucher, MC., Cheriet, F. (2020). An Interpretable Data-Driven Score for the Assessment of Fundus Images Quality. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_28
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DOI: https://doi.org/10.1007/978-3-030-50516-5_28
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