June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
An end-to-end deep learning method for predicting retinal layer thickness from Optical Coherence Tomography (OCT) images in patients with clinical progression of Age-related Macular Degeneration (AMD)
Author Affiliations & Notes
  • Qitong Gao
    Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States
  • Joshua Amason
    Department of Ophthalmology, Duke University, Durham, North Carolina, United States
  • Amanda Del Risco
    Duke University School of Medicine, Durham, North Carolina, United States
  • Praruj Pant
    Duke University School of Medicine, Durham, North Carolina, United States
  • Terry Lee
    Duke University School of Medicine, Durham, North Carolina, United States
  • Justin Ma
    Duke University School of Medicine, Durham, North Carolina, United States
  • Jennifer Minjia Chang-Wolf
    Duke University School of Medicine, Durham, North Carolina, United States
  • Jay Rathinavelu
    Duke University School of Medicine, Durham, North Carolina, United States
  • Aditya Kotla
    Duke University School of Medicine, Durham, North Carolina, United States
  • Miroslav Pajic
    Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, United States
  • Majda Hadziahmetovic
    Department of Ophthalmology, Duke University, Durham, North Carolina, United States
  • Footnotes
    Commercial Relationships   Qitong Gao None; Joshua Amason None; Amanda Del Risco None; Praruj Pant None; Terry Lee None; Justin Ma None; Jennifer Chang-Wolf None; Jay Rathinavelu None; Aditya Kotla None; Miroslav Pajic None; Majda Hadziahmetovic None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 3008 – F0278. doi:
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      Qitong Gao, Joshua Amason, Amanda Del Risco, Praruj Pant, Terry Lee, Justin Ma, Jennifer Minjia Chang-Wolf, Jay Rathinavelu, Aditya Kotla, Miroslav Pajic, Majda Hadziahmetovic; An end-to-end deep learning method for predicting retinal layer thickness from Optical Coherence Tomography (OCT) images in patients with clinical progression of Age-related Macular Degeneration (AMD). Invest. Ophthalmol. Vis. Sci. 2022;63(7):3008 – F0278.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To develop a fully-automated, end-to-end system that can capture and predict changes in retinal layer thickness from OCT scans.

Methods : We used 1308 OCT images (496 x 768 resolution) collected from 118 patients with AMD that progressed over time. Each patient dataset contained a consecutive sequence of OCT images obtained from multiple follow-up visits over at least two years (mean follow-up time 11.08 years ± 8.53). The region between the retinal pigment epithelium (RPE) and outer plexiform layer (OPL) was initially segmented by experts, and its thickness was presented by averaging the number of pixels between the two layers over the horizontal axis. Then, a Convolutional-Neural Network (CNN) was trained to capture the thicknesses from the longitudinal OCT scans, formulating thicknesses time-series for each patient. At last, a long-short term memory (LSTM) model was trained to process the time series and predict the thickness change in the future.

Results : OCT sequences in the dataset are split by 8:2 ratio to constitute training and testing dataset. In the test set, images obtained from t=0 to t=T-1 were used to evaluate the performance of the CNN, while t=T was left for evaluating the LSTM. The CNN achieved 13.0% mean absolute error (MAE) between the predicted thicknesses and the measurements extracted from segmentations. The LSTM achieved 21.46% MAE in predicting the RPE-OPL layer thickness at t=T, using the thickness time-series (t=0 toT-1) extrapolated by the CNN.

Conclusions : Our learning-based approach can facilitate predictions of retinal layer thickness changes directly from the OCT scans that could be used to predict AMD progression or response to treatment.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Training flow: (i) RPE-OPL layer segmentations was generated from the sequence of OCT images, from which the layer thickness was quantified. (ii) The CNN model was trained to capture the RPE-OPL layer thickness from the images in the sequence (t=0 to T-1). (iii) An LSTM model was used to predict the thickness at t=T.

Training flow: (i) RPE-OPL layer segmentations was generated from the sequence of OCT images, from which the layer thickness was quantified. (ii) The CNN model was trained to capture the RPE-OPL layer thickness from the images in the sequence (t=0 to T-1). (iii) An LSTM model was used to predict the thickness at t=T.

 

Visualization of the layer-thickness time-series and predictions for all the patients. The RPE-OPL thicknesses extracted from the segmentations are shown in black or blue. The thickness captured by the CNN model shown in green. The predicted layer thickness for t=T, by the LSTM model, are shown in orange.

Visualization of the layer-thickness time-series and predictions for all the patients. The RPE-OPL thicknesses extracted from the segmentations are shown in black or blue. The thickness captured by the CNN model shown in green. The predicted layer thickness for t=T, by the LSTM model, are shown in orange.

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