June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Longitudinal Precision of Vasculature Parameter Assessment on Ultra-widefield Fluorescein Angiography Using a Deep-Learning Model for Vascular Segmentation in Eyes without Vascular Pathology
Author Affiliations & Notes
  • Margaret O'Connell
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Duriye Damla Sevgi
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Sunil K Srivastava
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Jon Whitney
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Jenna M Hach
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Raymond Atwood
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Quinn Springer
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Justice Williams
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Amit Vasanji
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Jamie Reese
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Justis P Ehlers
    Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Margaret O'Connell, None; Duriye Damla Sevgi, None; Sunil Srivastava, Allergan (F), Bausch and Lomb (C), Gilead (F), Leica (P), Regeneron (F), Santen (C); Jon Whitney, ERT (E); Jenna Hach, None; Raymond Atwood, None; Quinn Springer, None; Justice Williams, None; Amit Vasanji, ERT (E); Jamie Reese, None; Justis Ehlers, Aerpio (F), Aerpio (C), Alcon (F), Alcon (C), Allegro (C), Allergan (F), Allergan (C), Genentech (F), Genentech/Roche (C), Leica (C), Leica (P), Novartis (F), Novartis (C), Regeneron (F), Regeneron (C), Santen (C), Thrombogenics/Oxurion (F), Thrombogenics/Oxurion (C), Zeiss (C)
  • Footnotes
    Support  RPB Unrestricted Grant to the Cole Eye Institute RPB1508DM, NIH K23-EY022947
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 2010. doi:
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      Margaret O'Connell, Duriye Damla Sevgi, Sunil K Srivastava, Jon Whitney, Jenna M Hach, Raymond Atwood, Quinn Springer, Justice Williams, Amit Vasanji, Jamie Reese, Justis P Ehlers; Longitudinal Precision of Vasculature Parameter Assessment on Ultra-widefield Fluorescein Angiography Using a Deep-Learning Model for Vascular Segmentation in Eyes without Vascular Pathology. Invest. Ophthalmol. Vis. Sci. 2020;61(7):2010.

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

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Abstract

Purpose : Longitudinal comparative measurements in ultra-widefield angiography (UWFA) can be confounded by multiple variables, including amount of fluorescein injected, eye exposure, image contrast, and image timing. Repeatability of longitudinal vasculature analysis has not been extensively explored. The purpose of this study was to evaluate the longitudinal consistency of retinal vasculature measures in phase-matched UWFA images utilizing a deep-learning augmented vascular segmentation system in eyes without retinal disease.

Methods : Eyes were identified with consecutive UWFA images. Following identification of the optimal phase-matched pairs, projection correction image alignment was performed and a common region of interest (ROI) capturing the same vasculature was determined for each pair. A trained automated deep-learning algorithm extracted the retinal vasculature from UWFA pairs. Geodesic diameter (GD) maps were created using ImageJ MorphoLibJ to measure the average distance between one central point (located at the macula or optic disc) and any point within the vascular network. Vasculature masks and GD maps were analyzed for vascular area, length and GD parameters.

Results : Twelve eyes of nine patients without any retinal vascular disease were identified and phase-matched. Mean total vessel area, length, disc-centered and macular GD were respectively 75.9E+4 ± 16.7E+4, 14.6E+4 ± 4.0E4, 971 ± 39 and 1090 ± 47 pixels in first time-point and 74.0E+4 ± 15.3E+4, 14.2E+4 ± 3.4E4, 963 ± 35 and 1084 ± 49 pixels in second time-point. No significant differences between time-points were identified in measures of vessel area, length and GD parameters.

Conclusions : Phase-matched UWFA longitudinal vasculature masks in eyes without vascular pathology demonstrate consistent measurements over time utilizing a deep-learning augmented segmentation model. Additional validation of vascular UWFA measurement stability over time in normal eyes will be explored in a larger dataset in the future, but this technique appears to hold promise as an opportunity for vascular feature interrogation in UWFA.

This is a 2020 ARVO Annual Meeting abstract.

 

Phase matched UWFA images (A, B) and their corresponding vasculature masks (C, D), color scaled disc centered GD maps (E, F), and color scaled macula centered GD maps (G, H) of an eye without vascular pathology.

Phase matched UWFA images (A, B) and their corresponding vasculature masks (C, D), color scaled disc centered GD maps (E, F), and color scaled macula centered GD maps (G, H) of an eye without vascular pathology.

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