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
Utilizing image denoising and machine Learning segmentation to quantify fluid volume in Eyes with vascular retinal diseases: the STATIC study
Author Affiliations & Notes
  • Niveditha Pattathil
    Vitreous Retina Macula Specialists of Toronto, Ontario, Canada
  • Netan Choudhry
    Vitreous Retina Macula Specialists of Toronto, Ontario, Canada
  • Mohammed Khan
    University of Toronto, Toronto, Ontario, Canada
  • Samantha Orr
    Vitreous Retina Macula Specialists of Toronto, Ontario, Canada
  • Amin Hatamnejad
    McMaster University, Hamilton, Ontario, Canada
  • John Golding
    Vitreous Retina Macula Specialists of Toronto, Ontario, Canada
  • Simrat Sodhi
    University of Cambridge, Cambridge, United Kingdom
  • Austin Pereira
    University of Toronto, Toronto, Ontario, Canada
  • Anuradha Dhawan
    Vitreous Retina Macula Specialists of Toronto, Ontario, Canada
  • Footnotes
    Commercial Relationships   Niveditha Pattathil None; Netan Choudhry Topcon, Optos, Bayer, Allergan, Novartis, Carl Zeiss Meditec, Ellex, Code C (Consultant/Contractor), Topcon, Optos, Carl Zeiss Meditec, Code F (Financial Support); Mohammed Khan None; Samantha Orr None; Amin Hatamnejad None; John Golding None; Simrat Sodhi None; Austin Pereira None; Anuradha Dhawan None
  • Footnotes
    Support  Research funding from Bayer
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 2399. doi:
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      Niveditha Pattathil, Netan Choudhry, Mohammed Khan, Samantha Orr, Amin Hatamnejad, John Golding, Simrat Sodhi, Austin Pereira, Anuradha Dhawan; Utilizing image denoising and machine Learning segmentation to quantify fluid volume in Eyes with vascular retinal diseases: the STATIC study. Invest. Ophthalmol. Vis. Sci. 2024;65(7):2399.

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

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Abstract

Purpose : To utilize a combination of a denoising algorithm with an automated OCT segmentation algorithm to quantify intraretinal fluid (IRF) and subretinal fluid (SRF) volumes in patients with retinal vascular diseases.

Methods : Swept-source OCT was used to acquire volume scans of the macula (Triton Topcon Healthcare, Tokyo, Japan) in patients with diabetic macular edema. Volume scans were processed with Topcon’s denoising algorithm set at 75%. Original and denoised images were then analyzed using an automated OCT segmentation algorithm which provided IRF and SRF volume measurements, which were compared to assess impact of denoising. Visual acuity (VA) was also collected. Fluid volumes were correlated with 6-month change in VA using the Pearson Correlation Coefficient (PCC); the difference in correlations was examined using a Wald test.

Results : Forty eyes were included in the final analysis. There was a significant difference between paired IRF volumes measured from denoised and original images (P<0.05). Paired SRF volumes were not significantly different (P > 0.05). The PCC between baseline IRF and 6-month change in VA revealed no correlation for the original images 0.07 (P = 0.649) or the denoised images 0.15 (P = 0.343). The difference between PCC values for denoised and original image baseline IRF measurement correlated to change in VA was significant (P<0.05). Baseline SRF was not correlated with the 6-month change in VA on original or denoised images, with PCC of 0.020 (P = 0.896) and 0.019 (P = 0.911), respectively. The difference in PCC between the denoised and original images for baseline SRF and change in VA was not significant (P>0.05). Subgroup analysis considering high and low image quality revealed no significant difference in impact of denoising between these groups (p>0.05).

Conclusions : Denoising led to a significant difference in the IRF volume measurement. Denoising has potential to improve weak OCT signals, as seen with the significant improvement in correlation between IRF and 6-month change in VA. Denoising does not improve correlations where no signal is present, as seen with the correlation between SRF and 6-month change in VA.

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

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