August 2019
Volume 60, Issue 11
Free
ARVO Imaging in the Eye Conference Abstract  |   August 2019
Automated volumetric segmentation of retinal fluid on optical coherence tomography using deep learning
Author Affiliations & Notes
  • Yukun Guo
    Casey Eye Institute, Portland, Oregon, United States
  • Honglian Xiong
    Casey Eye Institute, Portland, Oregon, United States
  • Tristan Hormel
    Casey Eye Institute, Portland, Oregon, United States
  • Jie Wang
    Casey Eye Institute, Portland, Oregon, United States
  • Thomas Hwang
    Casey Eye Institute, Portland, Oregon, United States
  • Yali Jia
    Casey Eye Institute, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Yukun Guo, None; Honglian Xiong, None; Tristan Hormel, None; Jie Wang, None; Thomas Hwang, None; Yali Jia, Optovue, Inc. (F), Optovue, Inc. (P)
  • Footnotes
    Support  National Institutes of Health (R01 EY027833, R01 EY024544, DP3 DK104397, P30 EY010572); Unrestricted Departmental Funding Grant and William & Mary Greve Special Scholar Award from Research to Prevent Blindness (New York, NY). National Natural Science Foundation of China (No.81601534)
Investigative Ophthalmology & Visual Science August 2019, Vol.60, 026. doi:
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    • Get Citation

      Yukun Guo, Honglian Xiong, Tristan Hormel, Jie Wang, Thomas Hwang, Yali Jia; Automated volumetric segmentation of retinal fluid on optical coherence tomography using deep learning. Invest. Ophthalmol. Vis. Sci. 2019;60(11):026.

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

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Abstract

Purpose : In diabetic macular edema (DME), thickening from intraretinal fluid and thinning from neural degeneration can occur concurrently and confound the understanding of disease progression and treatment response. Current quantitative methods only evaluate overall retinal thickness. We propose a deep learning-based method to automatically detect volumetric retinal fluid on optical coherence tomography (OCT).

Methods : 3 × 3-mm2 OCT scans were acquired on one eye by a 70-kHZ OCT commercial AngioVue system (RTVue-XR; Optove, Inc) from 51 participants in a clinical diabetic retinopathy (DR) study (45 with retinal edema and 6 healthy controls). A deep convolutional network with U-net-like architecture (Fig. 1) was constructed to detect and segment the retinal fluid volume. The network contains two types of input B-scans: structural OCT (Fig. 2A) and the corresponding OCTA (Fig. 2B). OCTA scans are used to enhance differentiation between retinal fluid and normal tissue. To improve the quality of OCT B-scans, two B-scans from adjacent positions were averaged. Experts manually delineated retinal fluid (red in Fig. 2C), non-fluid (black in Fig. 2C), and background area (green in Fig. 2C) on each B-scan to establish the ground truth. Data augmentation methods (horizontal flipping and Gaussian noise addition) enlarged the training data set. Six-fold cross-validation was used to evaluate the algorithm on the entire data set.

Results : Compared to manual delineation, the algorithm detected retinal fluid on B-scans (Fig. 2D) with an accuracy of (Dice coefficient) 89.1 ± 2.4% (mean ± standard deviation) on cross-validation data. Compiling adjacent B-scans generates 3-dimensional volumes of the retinal fluid with a smooth profile (Fig. 2E).

Conclusions : Our deep-learning method accurately segments retinal fluid volumetrically on OCT scans, providing a more complete picture of the anatomic changes in DME.

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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