June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Fully automated segmentation and quantification of the macular pigment burden detected on SS-OCTA and SD-OCT scans
Author Affiliations & Notes
  • Yuxuan Cheng
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Gissel Herrera
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Jie Lu
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Jianqing Li
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
    Ophthalmology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
  • Ziyu Liu
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Mengxi Shen
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Qinqin Zhang
    Carl Zeiss Meditec Inc, Dublin, California, United States
  • Jeremy Liu
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Xiao Zhou
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Farhan E. Hiya
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Yingying Shi
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Hao Zhou
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Giovanni Gregori
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Philip J Rosenfeld
    University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Ruikang K Wang
    Bioengineering, University of Washington, Seattle, Washington, United States
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Footnotes
    Commercial Relationships   Yuxuan Cheng None; Gissel Herrera None; Jie Lu None; Jianqing Li None; Ziyu Liu None; Mengxi Shen None; Qinqin Zhang Carl Zeiss Meditec , Code E (Employment); Jeremy Liu None; Xiao Zhou None; Farhan Hiya None; Yingying Shi None; Hao Zhou None; Giovanni Gregori Carl Zeiss Meditec, Code F (Financial Support); Philip Rosenfeld Annexon, Apellis, Bayer, Boehringer-Ingelheim, Carl Zeiss Meditec, Chengdu Kanghong Biotech, InflammX, Ocudyne, Regeneron, Unity Biotechnology, Code C (Consultant/Contractor), Alexion, Carl Zeiss Meditec, Gyroscope Therapeutics, Stealth BioTherapeutics, Code F (Financial Support), Apellis, Ocudyne, Valitor, Verana Health, Code I (Personal Financial Interest); Ruikang Wang Carl Zeiss Meditec, Code C (Consultant/Contractor), Carl Zeiss Meditec, Colgate Palmolive Company, Estee Lauder Inc , Code F (Financial Support)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 320. doi:
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    • Get Citation

      Yuxuan Cheng, Gissel Herrera, Jie Lu, Jianqing Li, Ziyu Liu, Mengxi Shen, Qinqin Zhang, Jeremy Liu, Xiao Zhou, Farhan E. Hiya, Yingying Shi, Hao Zhou, Giovanni Gregori, Philip J Rosenfeld, Ruikang K Wang; Fully automated segmentation and quantification of the macular pigment burden detected on SS-OCTA and SD-OCT scans. Invest. Ophthalmol. Vis. Sci. 2023;64(8):320.

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

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Abstract

Purpose : To automatically identify, segment, and compare the extent of hyperpigmentation using optical attenuation coefficients (OACs) with swept-source optical coherence tomography (SS-OCTA) and spectral domain (SD) OCT scans, patients were imaged both on SS-OCTA and SD-OCT instruments on the same day and the same algorithm was used to assess the total macular pigment burden.

Methods : Patients with age-related macular degeneration (AMD) and evidence of macular hyperpigmentation were imaged using the 6x6 mm scan patterns on both SS-OCTA (PLEX® Elite 9000; ZEISS, Dublin, CA) and SD-OCT (Cirrus® HD-OCT; ZEISS, Dublin, CA). The data cube of the SD-OCT system consisted of 200 A-scans per B-scans and 200 B-scans along the y-axis, while the data cube from the SS-OCTA system consisted of 500 A-scans per B-scans and 500 B-scans along the y-axis, each B-scan repeated twice at each position. OCT images were converted to depth-resolved OAC representations. The en face sum projection of the slab between inner limiting membrane and Bruch’s membrane was generated for pigment segmentation (Figure 1 A, B). An adaptive threshold algorithm was applied to compensate for the difference in image appearance between SD-OCT and SS-OCTA. A user-friendly software package was developed that automatically produced the binary pigment mask and the area of the total pigment burden (Figure 2). The area measurements of pigment burden on SD-OCT and SS-OCTA were compared with manual segmentation.

Results : A total of 24 eyes from 24 patients were included. The software segmented the hyperpigmentation on both SD-OCT and SS-OCTA scans. The mean area measurements of the total pigment burden from manual segmentation among all the cases was 0.062 ± 0.056mm2, while the mean area measurements from the algorithm using the SD-OCT and SS-OCTA scans were 0.096± 0.076mm2 and 0.112± 0.081mm2 respectively. The correlation between automated and manual segmentations of the total pigment burden was 0.83 for the SS-OCTA and 0.77 for the SD-OCT scans.(Figure1 C)

Conclusions : The automated algorithm was able to effectively and accurately identify, segment, and quantify the macular pigment burden using both SS-OCTA and SD-OCT scans. The SS-OCTA provided higher contrast images of the total pigment using OACs, which resulted in better agreement with the manual segmentations.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

 

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