June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
A novel method of enhancing in vivo OCT lamina cribrosa visualization for automated segmentation
Author Affiliations & Notes
  • Anse Vellappally
    Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York, United States
  • Palaiologos Alexopoulos
    Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York, United States
  • Zeinab Ghassabi
    Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York, United States
  • Dimitri Szezurek
    Department of Computer Science & Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Li Shijie
    Department of Computer Science & Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • TingFang Lee
    Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York, United States
    Departments of Population Health and Environmental Medicine, NYU Langone Health, New York, New York, United States
  • Jiyuan Hu
    Departments of Population Health and Environmental Medicine, NYU Langone Health, New York, New York, United States
  • Ronald Zambrano
    Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York, United States
  • Joel S Schuman
    Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York, United States
    Departments of Biomedical Engineering and Electrical & Computer Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Hiroshi Ishikawa
    Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York, United States
    Departments of Ophthalmology, Casey Eye Institute, and Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
  • James Fishbaugh
    Department of Computer Science & Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Guido Gerig
    Department of Computer Science & Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Gadi Wollstein
    Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York, United States
    Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Footnotes
    Commercial Relationships   Anse Vellappally None; Palaiologos Alexopoulos None; Zeinab Ghassabi None; Dimitri Szezurek None; Li Shijie None; TingFang Lee None; Jiyuan Hu None; Ronald Zambrano None; Joel Schuman Zeiss, Code P (Patent); Hiroshi Ishikawa None; James Fishbaugh None; Guido Gerig None; Gadi Wollstein None
  • Footnotes
    Support  NIH R01-EY030770, P30-EY013079, An unrestricted grant from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 234 – F0081. doi:
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    • Get Citation

      Anse Vellappally, Palaiologos Alexopoulos, Zeinab Ghassabi, Dimitri Szezurek, Li Shijie, TingFang Lee, Jiyuan Hu, Ronald Zambrano, Joel S Schuman, Hiroshi Ishikawa, James Fishbaugh, Guido Gerig, Gadi Wollstein; A novel method of enhancing in vivo OCT lamina cribrosa visualization for automated segmentation. Invest. Ophthalmol. Vis. Sci. 2022;63(7):234 – F0081.

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

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Abstract

Purpose : Automated segmentation of in-vivo lamina cribrosa (LC) has been challenging, owing to the complex 3D structure and decreased visibility in the lamina depth. Frangi’s vesselness filter, which was originally developed for angiogram segmentation, have been successfully demonstrated in segmenting the ex-vivo LC from micro-CT and second harmonic generation microscopy images. In this project we are proposing a new approach of segmenting the in vivo LC from OCT scans, incorporating the Frangi’s vesselness principle to facilitate in vivo LC image analysis in much greater detail compared to our previously described 3D analysis method.

Methods : In-vivo spectral-domain OCT scans (Leica, Chicago, IL) were acquired from healthy non-human primates. Scans of varying degree of image quality were selected for the analysis and underwent automated brightness and local contrast enhancement. 3D Frangi’s vesselness filter was applied using a fixed setting for scans of all qualities. Our previously described segmentation algorithm was then used to quantify the LC microstructure. The measurements generated from the Frangi analysis and from our own conventional method were compared with a standard reference (manually segmented LC by an expert). Paired t tests were performed to compare if the differences between standard reference and conventional method are greater than the differences between standard reference and Frangi analysis. The visibility of analyzable lamina and dice coefficient were also compared to the conventional method using the same test.

Results : In vivo scans acquired from 5 rhesus macaques (3 males, 1 female, aged 4.3-10.7 yrs) were used for the analysis. No significant difference was detected for LC microstructure parameters between Frangi’s approach and conventional method with respect to the standard reference, except for significantly higher pore count in Frangi’s method (p=0.003; Table). Furthermore, visibility (Figure) was significantly higher for the Frangi method compared to the conventional approach (p<0.001) with no difference detected for the semantic segmentation, as reflected by the dice coefficient.

Conclusions : The use of Frangi analysis substantially increase the analyzable lamina while providing similar quantification of the LC microstructure compared to our previous 3D analysis method. This improves the potential for automated and thorough volumetric analysis of in vivo OCT LC image.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

 

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