August 2021
Volume 62, Issue 11
Open Access
ARVO Imaging in the Eye Conference Abstract  |   August 2021
Polyp Detection from Structural and Angiographic OCT Using Deep Learning
Author Affiliations & Notes
  • Tristan Hormel
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Nida Wongchaisuwat
    Mahidol University, Salaya, Nakhon Pathom, Thailand
  • Nopasak Phasukkijwatana
    Mahidol University Faculty of Medicine Siriraj Hospital, Bangkok, Thailand
  • Xiaoyan Ding
    Sun Yat-Sen University Zhongshan Ophthalmic Center, Guangzhou, Guangdong, China
  • Steven T. Bailey
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Yali Jia
    Casey Eye Institute, Oregon Health & Science University, Portland, Oregon, United States
  • Footnotes
    Commercial Relationships   Tristan Hormel, None; Nida Wongchaisuwat, None; Nopasak Phasukkijwatana, None; Xiaoyan Ding, None; Steven Bailey, None; Yali Jia, Optos (I), Optovue (F), Optovue (I)
  • Footnotes
    Support  This work was supported by grant National Institutes of Health (R01 EY027833, R01 EY024544, R01 EY031394, P30 EY010572); Unrestricted Departmental Funding Grant and William & Mary Greve Special Scholar Award from Research to Prevent Blindness (New York, NY); Bright Focus Foundation (G2020168).
Investigative Ophthalmology & Visual Science August 2021, Vol.62, 82. doi:
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    • Get Citation

      Tristan Hormel, Nida Wongchaisuwat, Nopasak Phasukkijwatana, Xiaoyan Ding, Steven T. Bailey, Yali Jia; Polyp Detection from Structural and Angiographic OCT Using Deep Learning. Invest. Ophthalmol. Vis. Sci. 2021;62(11):82.

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

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Abstract

Purpose : We aim to automatically detect polyps characteristic of polypoidal choroidal vasculopathy (PCV) from structural and angiographic OCT using a deep learning network.

Methods : This study included 66 eyes diagnosed with PCV using a combination of indocyanine green angiography (ICGA) and structural OCT. From these eyes, 13 were used for validation, 13 for testing, and the rest for training. Thirteen healthy controls were also included for performance evaluation. Volunteers were scanned with a commercial instrument using either a 3×3-mm or 6×6-mm scan pattern at the location of polyps as revealed by ICGA. No scans were excluded due to image quality. We used a faster region-based convolutional neural network (faster RCNN) architecture, which is a region proposal network that detects features within learned regions (Fig. 1). To aid in feature identification, we restricted region proposals to locations between the inner limiting and Bruch’s membranes. Ground truth region proposals were determined by cross-referencing ICGA images with the structural OCT scans. Inputs to the network consisted of sets of 10 adjacent structural OCT and the corresponding OCT angiography (OCTA) B-frames. The network outputs regions of interest and the probability that each contains a polyp (Fig. 2).

Results : We evaluated performance by determining the area under receiver operating characteristic curve (AROC = 0.938) (Fig. 2) for polyp detection by comparison with the healthy controls. The network achieves a detection sensitivity of 73.1% at 95% specificity.

Conclusions : A deep learning approach can reliably detect polyps from structural OCT and OCTA data. This approach may help enable improved monitoring and screening for PCV by removing the need for invasive and expensive ICGA procedures.

This is a 2021 Imaging in the Eye Conference abstract.

 

 

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