Physics-informed deep generative learning for quantitative assessment of the retina
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-50911-y |
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| author | 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 Simon Walker-Samuel |
| author_facet | 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 Simon Walker-Samuel |
| author_sort | Emmeline E. Brown |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0bd85a757e2e4546b32f953ae15e0ae5 |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-0bd85a757e2e4546b32f953ae15e0ae52025-08-20T02:48:22ZengNature PortfolioNature Communications2041-17232024-08-0115111410.1038/s41467-024-50911-yPhysics-informed deep generative learning for quantitative assessment of the retinaEmmeline E. Brown0Andrew A. Guy1Natalie A. Holroyd2Paul W. Sweeney3Lucie Gourmet4Hannah Coleman5Claire Walsh6Athina E. Markaki7Rebecca Shipley8Ranjan Rajendram9Simon Walker-Samuel10Centre for Computational Medicine, University College LondonCentre for Computational Medicine, University College LondonCentre for Computational Medicine, University College LondonCancer Research UK Cambridge Institute, University of CambridgeCentre for Computational Medicine, University College LondonCentre for Computational Medicine, University College LondonCentre for Computational Medicine, University College LondonDepartment of Engineering, University of CambridgeCentre for Computational Medicine, University College LondonMoorfields Eye HospitalCentre for Computational Medicine, University College LondonAbstract 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.https://doi.org/10.1038/s41467-024-50911-y |
| spellingShingle | 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 Simon Walker-Samuel Physics-informed deep generative learning for quantitative assessment of the retina Nature Communications |
| title | Physics-informed deep generative learning for quantitative assessment of the retina |
| title_full | Physics-informed deep generative learning for quantitative assessment of the retina |
| title_fullStr | Physics-informed deep generative learning for quantitative assessment of the retina |
| title_full_unstemmed | Physics-informed deep generative learning for quantitative assessment of the retina |
| title_short | Physics-informed deep generative learning for quantitative assessment of the retina |
| title_sort | physics informed deep generative learning for quantitative assessment of the retina |
| url | https://doi.org/10.1038/s41467-024-50911-y |
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