Physics-informed deep generative learning for quantitative assessment of the retina

Emmeline E. Brown, Andrew A. Guy, Natalie A. Holroyd, Paul W. Sweeney, Lucie Gourmet, Hannah Coleman, Claire Walsh, Athina E. Markaki, Rebecca Shipley, Ranjan Rajendram and Simon Walker-Samuel ()
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Emmeline E. Brown: University College London
Andrew A. Guy: University College London
Natalie A. Holroyd: University College London
Paul W. Sweeney: University of Cambridge
Lucie Gourmet: University College London
Hannah Coleman: University College London
Claire Walsh: University College London
Athina E. Markaki: University of Cambridge
Rebecca Shipley: University College London
Ranjan Rajendram: Moorfields Eye Hospital
Simon Walker-Samuel: University College London

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks with no human input and which out-performs human labelling. Segmentation of DRIVE and STARE retina photograph datasets provided near state-of-the-art vessel segmentation, with training on only a small (n = 100) simulated dataset. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.

Date: 2024
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DOI: 10.1038/s41467-024-50911-y

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