Medical Image Quality Metrics for Retinal Scans

Investigating and developing tools to automatically assess the quality of retinal scans

A low-quality retinal scan.

There are a number of research projects using machine learning on retinal image scans to detect and identify various conditions such as Age-related Macular Degeneration (AMD), Diabetic Retinopathy, as well as a variety of inherited conditions such as Stargardts and Retinitis Pigmentosa. These approaches all apply to retinal scans taken from the same machine, in a handful of different imaging modalities.

However, frequently the available data comes in an uncurated form, and scan quality can vary significantly. Hence it would be of great use to have some form of automated quality assessment tool that could filter out low-quality scans automatically. This is also of importance to future deployment of these algorithms to ensure they are not applied to low-quality scans.

In this project we aim to investigate various methods of assessing quality of retinal scans, and ultimately develop a universal approach to retinal image quality assessment. This includes looking at “objective” metrics, as well as looking towards some machine-learning based approaches. This will involve working with multiple teams working with retinal images to understand the different challenges associated with evaluating scans from eyes with different conditions.

Leads

William Woof

Postdoc Medical Imaging and AI

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Advaith Yoga Veturi

Honorary Research Assistant Medical Imaging and AI

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We are always eager for hard-working & motivated people to come and work with us.

If you're interested in joining us, please send Nikolas Pontikos a quick email with a CV and personal statement