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Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.

Publication ,  Journal Article
Soltanian-Zadeh, S; Liu, Z; Liu, Y; Lassoued, A; Cukras, CA; Miller, DT; Hammer, DX; Farsiu, S
Published in: Biomedical optics express
February 2023

Objective quantification of photoreceptor cell morphology, such as cell diameter and outer segment length, is crucial for early, accurate, and sensitive diagnosis and prognosis of retinal neurodegenerative diseases. Adaptive optics optical coherence tomography (AO-OCT) provides three-dimensional (3-D) visualization of photoreceptor cells in the living human eye. The current gold standard for extracting cell morphology from AO-OCT images involves the tedious process of 2-D manual marking. To automate this process and extend to 3-D analysis of the volumetric data, we propose a comprehensive deep learning framework to segment individual cone cells in AO-OCT scans. Our automated method achieved human-level performance in assessing cone photoreceptors of healthy and diseased participants captured with three different AO-OCT systems representing two different types of point scanning OCT: spectral domain and swept source.

Duke Scholars

Published In

Biomedical optics express

DOI

EISSN

2156-7085

ISSN

2156-7085

Publication Date

February 2023

Volume

14

Issue

2

Start / End Page

815 / 833

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4003 Biomedical engineering
  • 3212 Ophthalmology and optometry
  • 0912 Materials Engineering
  • 0205 Optical Physics
 

Citation

APA
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ICMJE
MLA
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Soltanian-Zadeh, S., Liu, Z., Liu, Y., Lassoued, A., Cukras, C. A., Miller, D. T., … Farsiu, S. (2023). Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes. Biomedical Optics Express, 14(2), 815–833. https://doi.org/10.1364/boe.478693
Soltanian-Zadeh, Somayyeh, Zhuolin Liu, Yan Liu, Ayoub Lassoued, Catherine A. Cukras, Donald T. Miller, Daniel X. Hammer, and Sina Farsiu. “Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomedical Optics Express 14, no. 2 (February 2023): 815–33. https://doi.org/10.1364/boe.478693.
Soltanian-Zadeh S, Liu Z, Liu Y, Lassoued A, Cukras CA, Miller DT, et al. Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes. Biomedical optics express. 2023 Feb;14(2):815–33.
Soltanian-Zadeh, Somayyeh, et al. “Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes.Biomedical Optics Express, vol. 14, no. 2, Feb. 2023, pp. 815–33. Epmc, doi:10.1364/boe.478693.
Soltanian-Zadeh S, Liu Z, Liu Y, Lassoued A, Cukras CA, Miller DT, Hammer DX, Farsiu S. Deep learning-enabled volumetric cone photoreceptor segmentation in adaptive optics optical coherence tomography images of normal and diseased eyes. Biomedical optics express. 2023 Feb;14(2):815–833.
Journal cover image

Published In

Biomedical optics express

DOI

EISSN

2156-7085

ISSN

2156-7085

Publication Date

February 2023

Volume

14

Issue

2

Start / End Page

815 / 833

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4003 Biomedical engineering
  • 3212 Ophthalmology and optometry
  • 0912 Materials Engineering
  • 0205 Optical Physics