1. Physics-informed deep generative learning for quantitative assessment of the retina.
- Author
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Brown EE, Guy AA, Holroyd NA, Sweeney PW, Gourmet L, Coleman H, Walsh C, Markaki AE, Shipley R, Rajendram R, and Walker-Samuel S
- Subjects
- Humans, Image Processing, Computer-Assisted methods, Diabetic Retinopathy diagnosis, Macular Degeneration pathology, Deep Learning, Retinal Vessels diagnostic imaging, Retina diagnostic imaging, Algorithms
- 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., (© 2024. The Author(s).)
- Published
- 2024
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