Physics-informed deep generative learning for quantitative assessment of the retina.
Nat Commun
; 15(1): 6859, 2024 Aug 10.
Article
in En
| MEDLINE
| ID: mdl-39127778
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Retina
/
Retinal Vessels
/
Algorithms
/
Deep Learning
Limits:
Humans
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2024
Document type:
Article
Country of publication:
Reino Unido