July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Detecting Retinal Nerve Fiber Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm
Author Affiliations & Notes
  • Alessandro A Jammal
    Visual Performance Laboratory, Duke University, Durham, North Carolina, United States
    Department of Ophthalmology, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
  • Atalie C. Thompson
    Visual Performance Laboratory, Duke University, Durham, North Carolina, United States
  • Nara Ogata
    Visual Performance Laboratory, Duke University, Durham, North Carolina, United States
  • Eduardo Bicalho Mariottoni
    Visual Performance Laboratory, Duke University, Durham, North Carolina, United States
  • Carla Urata
    Visual Performance Laboratory, Duke University, Durham, North Carolina, United States
  • Vital P Costa
    Department of Ophthalmology, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
  • Felipe A Medeiros
    Visual Performance Laboratory, Duke University, Durham, North Carolina, United States
  • Footnotes
    Commercial Relationships   Alessandro Jammal, None; Atalie Thompson, None; Nara Ogata, None; Eduardo Mariottoni, None; Carla Urata, None; Vital Costa, None; Felipe Medeiros, Allergan (C), Allergan (F), Bausch&Lomb (F), Carl Zeiss Meditec (C), Carl Zeiss Meditec (F), Heidelberg Engineering (F), Merck (F), nGoggle Inc. (F), Novartis (C), Reichert (C), Reichert (R), Sensimed (C), Topcon (C)
  • Footnotes
    Support  Supported in part by the National Institutes of Health/National Eye Institute grant EY027651(FAM), EY025056 (FAM), EY021818 (FAM), and Conselho Nacional de Desenvolvimento Científico e Tecnológico (AAJ).
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2209. doi:https://doi.org/
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    • Get Citation

      Alessandro A Jammal, Atalie C. Thompson, Nara Ogata, Eduardo Bicalho Mariottoni, Carla Urata, Vital P Costa, Felipe A Medeiros; Detecting Retinal Nerve Fiber Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2209. doi: https://doi.org/.

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

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Abstract

Purpose : To propose a deep learning algorithm that detects errors in retinal never fiber layer (RNFL) segmentation from spectral-domain optical coherence tomography (SDOCT) B-scans.

Methods : A cross-sectional study with 25,250 OCT B-scans reviewed for segmentation errors from 1,363 eyes of 709 subjects. The sample was randomly divided into validation plus training (80%) and test (20%) sets. SDOCT B-scans that had been labeled for quality by human graders were used as the reference standard to train a deep learning convolutional neural network to detect RNFL segmentation errors. The performance of the deep learning algorithm was evaluated in the test sample by outputting a probability of having a segmentation error for each B-scan. The accuracy and the ability of the algorithm to detect segmentation errors was evaluated with the area under the receiver operating characteristic curve (ROC).

Results : Mean deep learning probabilities of segmentation error in the test sample were 0.91 ± 0.17 vs. 0.12 ± 0.21 (P<0.001) for scans with and without segmentation errors, respectively. The deep learning algorithm had an area under the ROC curve of 0.981 (95% CI: 0.975 to 0.986) with an overall accuracy of 92.8%.

Conclusions : We introduced a novel deep learning approach to assess RNFL segmentation errors in SDOCT B-scans. This algorithm can help clinicians and researchers review images for artifacts in SDOCT tests in a timely manner and avoid inaccurate diagnostic interpretations.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Figure 1. Spectral-domain optical coherence tomography (SDOCT) B-scans with segmentation errors correctly detected by both the reading center and the deep learning algorithm. Class activation maps (heatmaps) on the right show the regions of the B-scans that had greatest weight in the deep learning algorithm classification. It is possible to see that the deep learning algorithm identified (A) errors of segmentation involving both the delineation of the internal limiting membrane, (B) as well as the posterior boundary of the RNFL, (C) multiple errors in the same scan, and (D) even very small segmentation errors.

Figure 1. Spectral-domain optical coherence tomography (SDOCT) B-scans with segmentation errors correctly detected by both the reading center and the deep learning algorithm. Class activation maps (heatmaps) on the right show the regions of the B-scans that had greatest weight in the deep learning algorithm classification. It is possible to see that the deep learning algorithm identified (A) errors of segmentation involving both the delineation of the internal limiting membrane, (B) as well as the posterior boundary of the RNFL, (C) multiple errors in the same scan, and (D) even very small segmentation errors.

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