August 2019
Volume 60, Issue 11
Free
ARVO Imaging in the Eye Conference Abstract  |   August 2019
Automated Detection of Retinal Fluid Using a Convolutional Neural Network
Author Affiliations & Notes
  • Tristan Hormel
    Casey Eye, Oregon Health and Science University, Portland, Oregon, United States
  • JIE WANG
    Casey Eye, Oregon Health and Science University, Portland, Oregon, United States
  • Qisheng You
    Casey Eye, Oregon Health and Science University, Portland, Oregon, United States
  • David Huang
    Casey Eye, Oregon Health and Science University, Portland, Oregon, United States
  • Thomas Hwang
    Casey Eye, Oregon Health and Science University, Portland, Oregon, United States
  • Yali Jia
    Casey Eye, Oregon Health and Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Tristan Hormel, None; JIE WANG, None; Qisheng You, None; David Huang, None; Thomas Hwang, None; Yali Jia, None
  • Footnotes
    Support  This work was supported by grants R01EY027833, DP3 DK104397, P30 EY010572 from the National Institutes of Health (Bethesda, MD), and by and William & Mary Greve Special Scholar Award and unrestricted departmental funding from Research to Prevent Blindness (New York, NY).
Investigative Ophthalmology & Visual Science August 2019, Vol.60, PB087. doi:https://doi.org/
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    • Get Citation

      Tristan Hormel, JIE WANG, Qisheng You, David Huang, Thomas Hwang, Yali Jia; Automated Detection of Retinal Fluid Using a Convolutional Neural Network. Invest. Ophthalmol. Vis. Sci. 2019;60(11):PB087. doi: https://doi.org/.

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

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Abstract

Purpose : Diabetic macular edema (DME) is a major cause of vision loss and is not amenable to screening with photographic methods. Retinal fluid (RF) on optical coherence tomography (OCT) may be an earlier sign than macular thickening to identify eyes at risk for vision loss from DME. We developed a convolutional neural network (CNN) for automated detection of RF in OCT volumes.

Methods : A commercial spectral-domain OCT system (RTVue-XR, Optovue, Inc.) captured OCT macular scans (304×304 A-lines covering 3×3 or 6×6 mm2 area) from both eyes of a total 172 patients with diabetes and generated 2150 scan volumes by including repeat visits. An expert graded each volume for the presence of fluid. We reserved 200 volumes for testing and used the remainder for training. We did not exclude any scans due to poor image quality.
Our CNN architecture consists of data compression, feature extraction and decision-making steps (Fig. 1). Data compression was accomplished through successive convolution, batch normalization and rectified linear unit activation. Feature extraction was achieved by additionally including max pooling layers. Finally, fully connected layers and a softmax activation function made the prediction. Volumes with a probability over 50% of containing RF were classified as positive for its presence.

Results : Compared to expert manual grading, the trained network classified the volumes for the presence of RF with an accuracy of 83.33%, a precision of 84.62%, and recall of 81.05% on the test data set. Fig. 2 shows example B-scans with the associated detection probability.

Conclusions : Our CNN detects RF with high accuracy on a diverse data set regardless of scan quality or severity of disease. Volumetric evaluation obviates the need for selection of specific B-scans. Inclusion of both 3×3 and 6×6 mm2 scans makes our results agnostic to scan dimensions.

This abstract was presented at the 2019 ARVO Imaging in the Eye Conference, held in Vancouver, Canada, April 26-27, 2019.

 

 

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