July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Development and validation of a deep learning algorithm for distinguishing capillary dropout from signal reduction artifacts on OCT angiography
Author Affiliations & Notes
  • Yukun Guo
    Casey Eye Institute, OHSU, Portland, Oregon, United States
  • Tristan Hormel
    Casey Eye Institute, OHSU, Portland, Oregon, United States
  • Acner Camino
    Casey Eye Institute, OHSU, Portland, Oregon, United States
  • Jie Wang
    Casey Eye Institute, OHSU, Portland, Oregon, United States
  • David Huang
    Casey Eye Institute, OHSU, Portland, Oregon, United States
  • Thomas S Hwang
    Casey Eye Institute, OHSU, Portland, Oregon, United States
  • Yali Jia
    Casey Eye Institute, OHSU, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Yukun Guo, None; Tristan Hormel, None; Acner Camino, Optovue, Inc. (P); Jie Wang, None; David Huang, Optovue, Inc. (I), Optovue, Inc. (P), Optovue, Inc. (F); Thomas Hwang, None; Yali Jia, Optovue, Inc. (P), Optovue, Inc. (F)
  • 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).
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 2208. doi:https://doi.org/
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    • Get Citation

      Yukun Guo, Tristan Hormel, Acner Camino, Jie Wang, David Huang, Thomas S Hwang, Yali Jia; Development and validation of a deep learning algorithm for distinguishing capillary dropout from signal reduction artifacts on OCT angiography. Invest. Ophthalmol. Vis. Sci. 2019;60(9):2208. doi: https://doi.org/.

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

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Abstract

Purpose : Avascular areas shown on retinal optical coherence tomography angiography (OCTA) can result from either capillary dropout or signal reduction effects (shadowing or defocusing). A deep learning algorithm is developed to distinguish real capillary dropout area from artifacts.

Methods : 6 × 6-mm2 OCTA scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optove, Inc) from 180 participants in a clinical diabetic retinopathy (DR) study (76 healthy controls, 34 participants with diabetes without retinopathy, 31 participants with mild or moderate nonproliferative (NPDR) and 39 participants with severe DR). No scans were excluded due to image quality. OCTA scans were also acquired from 12 healthy volunteers, with signal strength reduction manufactured by defocus and shadow. A U-net-like convolutional neural network was built to detect the avascular area due to perfusion loss (blue in Fig. 1F) or signal reduction artifact (yellow in Fig. 1F). The input to the network contains the en fac angiogram of the superficial vascular complex (SVC), the mean projection of inner retinal reflectance (Gaussian filtered) and the thickness map of the inner retina. The ground truth of the two types of areas were manually graded by retinal experts. Six-fold cross-validation was used to evaluate our algorithm on the entire dataset.

Results : On the tested scans from normal controls, we found that the algorithm can detect the actual foveal avascular area, as well as the areas affected by manufactured signal reduction from either defocusing with 1-3 diopters (Fig. 2A-2B), vignetting (Fig. 2C), artificial shadows (Fig. 2D), and the areas affected by natural media opacity (cataract and vitreous floaters, etc. Fig. 2E). On the tested scans from clinical DR study, we found that the algorithm can classify avascular areas related to perfusion defect or signal reduction (Fig. 2F). By averaging the results from six cross-validations, the detection accuracy of perfusion related avascular area reached 92.58±2.30% (mean ± standard deviation).

Conclusions : Our method can distinguish perfusion defects from the signal reduction on retinal OCTA, which will allow more scans included in the analysis and make the quantitative measurements on retinal ischemia more reliable.

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

 

 

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