Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Menten:2023:10.1016/j.xops.2023.100294,
author = {Menten, MJ and Holland, R and Leingang, O and Bogunovic, H and Hagag, AM and Kaye, R and Riedl, S and Traber, GL and Hassan, ON and Pawlowski, N and Glocker, B and Fritsche, LG and Scholl, HPN and Sivaprasad, S and Schmidt-Erfurth, U and Rueckert, D and Lotery, AJ},
doi = {10.1016/j.xops.2023.100294},
journal = {Ophthalmology Science},
pages = {1--10},
title = {Exploring healthy retinal aging with deep learning},
url = {http://dx.doi.org/10.1016/j.xops.2023.100294},
volume = {3},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PurposeTo study the individual course of retinal changes caused by healthy aging using deep learning.DesignRetrospective analysis of a large data set of retinal OCT images.ParticipantsA total of 85 709 adults between the age of 40 and 75 years of whom OCT images were acquired in the scope of the UK Biobank population study.MethodsWe created a counterfactual generative adversarial network (GAN), a type of neural network that learns from cross-sectional, retrospective data. It then synthesizes high-resolution counterfactual OCT images and longitudinal time series. These counterfactuals allow visualization and analysis of hypothetical scenarios in which certain characteristics of the imaged subject, such as age or sex, are altered, whereas other attributes, crucially the subject’s identity and image acquisition settings, remain fixed.Main Outcome MeasuresUsing our counterfactual GAN, we investigated subject-specific changes in the retinal layer structure as a function of age and sex. In particular, we measured changes in the retinal nerve fiber layer (RNFL), combined ganglion cell layer plus inner plexiform layer (GCIPL), inner nuclear layer to the inner boundary of the retinal pigment epithelium (INL-RPE), and retinal pigment epithelium (RPE).ResultsOur counterfactual GAN is able to smoothly visualize the individual course of retinal aging. Across all counterfactual images, the RNFL, GCIPL, INL-RPE, and RPE changed by −0.1 μm ± 0.1 μm, −0.5 μm ± 0.2 μm, −0.2 μm ± 0.1 μm, and 0.1 μm ± 0.1 μm, respectively, per decade of age. These results agree well with previous studies based on the same cohort from the UK Biobank population study. Beyond population-wide average measures, our counterfactual GAN allows us to explore whether the retinal layers of a given eye will increase in thickness, decrease in thickness, or stagnate as a subject ages.ConclusionThis study demonstrates how counterfactual GANs
AU  - Menten,MJ
AU  - Holland,R
AU  - Leingang,O
AU  - Bogunovic,H
AU  - Hagag,AM
AU  - Kaye,R
AU  - Riedl,S
AU  - Traber,GL
AU  - Hassan,ON
AU  - Pawlowski,N
AU  - Glocker,B
AU  - Fritsche,LG
AU  - Scholl,HPN
AU  - Sivaprasad,S
AU  - Schmidt-Erfurth,U
AU  - Rueckert,D
AU  - Lotery,AJ
DO  - 10.1016/j.xops.2023.100294
EP  - 10
PY  - 2023///
SN  - 2666-9145
SP  - 1
TI  - Exploring healthy retinal aging with deep learning
T2  - Ophthalmology Science
UR  - http://dx.doi.org/10.1016/j.xops.2023.100294
UR  - http://hdl.handle.net/10044/1/103539
VL  - 3
ER  -
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