Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
Deep learning-based scatterer density estimation reveals age-related reduction of scatterers in inner retinal layers
Author Affiliations & Notes
  • Thitiya Seesan
    Computational Optics Group, Tsukuba Daigaku, Tsukuba, Ibaraki, Japan
  • Shuichi Makita
    Computational Optics Group, Tsukuba Daigaku, Tsukuba, Ibaraki, Japan
    Ophthalmology, Tokyo Ika Daigaku Ibaraki Iryo Center, Inashiki-gun, Ibaraki, Japan
  • Masahiro Miura
    Ophthalmology, Tokyo Ika Daigaku Ibaraki Iryo Center, Inashiki-gun, Ibaraki, Japan
  • Kosei Yanagida
    Ophthalmology, Tokyo Ika Daigaku Ibaraki Iryo Center, Inashiki-gun, Ibaraki, Japan
  • Atsuya Miki
    Department of Myopia Control Research, Aichi Ika Daigaku, Nakakute, Aichi, Japan
  • Yoshiaki Yasuno
    Computational Optics Group, Tsukuba Daigaku, Tsukuba, Ibaraki, Japan
    Ophthalmology, Tokyo Ika Daigaku Ibaraki Iryo Center, Inashiki-gun, Ibaraki, Japan
  • Footnotes
    Commercial Relationships   Thitiya Seesan Topcon, Nikon, Yokogawa, Skytechnology, Kao, Code F (Financial Support); Shuichi Makita Topcon, Nikon, Yokogawa, Skytechnology, Kao, Code F (Financial Support); Masahiro Miura Santen, Sandoz, Altos, Code F (Financial Support), Santen, Kowa, Novartis, Code R (Recipient); Kosei Yanagida None; Atsuya Miki Menicon, SEED, Code F (Financial Support); Yoshiaki Yasuno Topcon, Nikon, Yokogawa, Skytechnology, Kao, Code F (Financial Support)
  • Footnotes
    Support  18K09460, 18H01893, 21H01836, 22K04962, 21K09684, JPMJCR2105
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 1388. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Thitiya Seesan, Shuichi Makita, Masahiro Miura, Kosei Yanagida, Atsuya Miki, Yoshiaki Yasuno; Deep learning-based scatterer density estimation reveals age-related reduction of scatterers in inner retinal layers. Invest. Ophthalmol. Vis. Sci. 2024;65(7):1388.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Retinal ganglion cells (GCs) are known to be sensitive to aging and glaucoma. But OCT can only indirectly access the GCs through the layer thicknesses.

We use our developed deep-learning based scatterer density estimator (SDE) to access the retinal aging. Since the scatterer density (ScD) of GC-related layers may indicate the densities of GC, its axon, and synapse, the SDE might be capable of assessing the are-related alteration of GC-related tissues.

Methods : Our SDE is a neural-network based parameter estimator trained by fully numerically simulated OCT speckle image patches (16 x 16 pixels). This was trained by 80,000 images patches (16 x 16 pixels) and estimates a ScD from a patch.

6 × 6 mm2 macular OCT images were acquired from 24 eyes of 24 normal subjects [41.2 ± 12.9 y/o (mean ± standard deviation, ranges from 20 to 63 y/o)]. The nerve fiber layer (NFL), GC layer (GCL), and inner plexiform layer (IPL) were segmented by Iowa reference algorithm. The retina was sectorized as shown in Fig. 1. Three more sectors, i.e., the combined-inner and outer rings and whole area of 5.5-mm circle were also used. For each sector-layer combinations (SLCs), the mean ScD and OCT intensity were computed. The correlation between the age and mean ScD and OCT were computed. GC complex (GCC = NFL + GCL + IPL) thickness vs age correlation was also computed.

Results : Fig. 1 shows the mean ScD and OCT intensity, and Fig. 2 summarizes the correlations. The regression lines were shown only if the correlation was significant. Although GCC thickness did not show significant correlation with age, ScD showed moderate negative correlations to the age (r = -0.55, -0.53, -0.54, for NFL, GCL, and IPL) for the whole 5.5-mm sector. The OCT intensity also showed significant but weaker correlations than ScD (r = -0.52, -0.40, and -0.44 for NFL, GCL, and IPL).

The number of significant sectors for ScD vs OCT were 8 vs 7 (ScD vs OCT, for NFL), 3 vs 5 (for GCL), and 9 vs 7 (for IPL). At 19 SCLs, ScD showed higher correlations than OCT (marked with red *), while OCT showed higher correlation only at 3 SLCs (blue *).

Conclusions : ScD of GC-related layers would be a better indicator of age than the OCT signal intensity and the GCC thickness.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

 

The mean ScD and OCT intensity.

The mean ScD and OCT intensity.

 

Correlations of age vs ScD and OCT intensity.

Correlations of age vs ScD and OCT intensity.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×