Literature DB >> 29946495

The Reliability of Cone Density Measurements in the Presence of Rods.

Jessica I W Morgan1,2, Grace K Vergilio1, Jessica Hsu3, Alfredo Dubra4, Robert F Cooper1,5.   

Abstract

PURPOSE: Recent advances in adaptive optics scanning light ophthalmoscopy (AOSLO) have enabled visualization of cone inner segments through nonconfocal split-detection, in addition to rod and cone outer segments revealed by confocal reflectance. Here, we examined the interobserver reliability of cone density measurements in both AOSLO imaging modalities.
METHODS: Five normal subjects (nine eyes) were imaged along the horizontal and vertical meridians using a custom AOSLO with confocal and nonconfocal split-detection modalities. The resulting images were montaged using a previously described semiautomatic algorithm. Regions of interest (ROIs) were selected from the confocal montage at 190 μm, and from split-detection and confocal montages at 900 and 1800 μm from the fovea. Four observers (three experts, one naïve) manually identified cone locations in each ROI, and these locations were used to calculate bound densities. Intraclass correlation coefficients and Dice's coefficients were calculated to assess interobserver agreement.
RESULTS: Interobserver agreement was high in cone-only images (confocal 190 μm: 0.85; split-detection 900 μm: 0.91; split-detection 1800 μm: 0.89), moderate in confocal images at 900 μm (0.68), and poor in confocal images at 1800 μm (0.24). Excluding the naïve observer data substantially increased agreement within confocal images (190 μm: 0.99; 900 μm: 0.80; 1800 μm: 0.68).
CONCLUSIONS: Interobserver measurements of cone density are more reliable in rod-free retinal images. Moreover, when using manual cell identification, it is essential that observers are trained, particularly for confocal AOSLO images. TRANSLATIONAL RELEVANCE: This study underscores the need for additional reliability studies in eyes containing pathology where identifying cones can be substantially more difficult.

Entities:  

Keywords:  adaptive optics; cone density; photoreceptors; split detection

Year:  2018        PMID: 29946495      PMCID: PMC6016505          DOI: 10.1167/tvst.7.3.21

Source DB:  PubMed          Journal:  Transl Vis Sci Technol        ISSN: 2164-2591            Impact factor:   3.283


  23 in total

1.  Automated identification of cone photoreceptors in adaptive optics retinal images.

Authors:  Kaccie Y Li; Austin Roorda
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2007-05       Impact factor: 2.129

2.  Multi-modal automatic montaging of adaptive optics retinal images.

Authors:  Min Chen; Robert F Cooper; Grace K Han; James Gee; David H Brainard; Jessica I W Morgan
Journal:  Biomed Opt Express       Date:  2016-11-03       Impact factor: 3.732

3.  Automated Photoreceptor Cell Identification on Nonconfocal Adaptive Optics Images Using Multiscale Circular Voting.

Authors:  Jianfei Liu; HaeWon Jung; Alfredo Dubra; Johnny Tam
Journal:  Invest Ophthalmol Vis Sci       Date:  2017-09-01       Impact factor: 4.799

4.  Reliability and Repeatability of Cone Density Measurements in Patients with Congenital Achromatopsia.

Authors:  Mortada A Abozaid; Christopher S Langlo; Adam M Dubis; Michel Michaelides; Sergey Tarima; Joseph Carroll
Journal:  Adv Exp Med Biol       Date:  2016       Impact factor: 2.622

5.  Reliability of cone counts using an adaptive optics retinal camera.

Authors:  Mélanie Bidaut Garnier; Mathieu Flores; Guillaume Debellemanière; Marc Puyraveau; Perle Tumahai; Mathieu Meillat; Claire Schwartz; Michel Montard; Bernard Delbosc; Maher Saleh
Journal:  Clin Exp Ophthalmol       Date:  2014-07-25       Impact factor: 4.207

6.  In vivo autofluorescence imaging of the human and macaque retinal pigment epithelial cell mosaic.

Authors:  Jessica I W Morgan; Alfredo Dubra; Robert Wolfe; William H Merigan; David R Williams
Journal:  Invest Ophthalmol Vis Sci       Date:  2008-10-24       Impact factor: 4.799

Review 7.  The fundus photo has met its match: optical coherence tomography and adaptive optics ophthalmoscopy are here to stay.

Authors:  Jessica I W Morgan
Journal:  Ophthalmic Physiol Opt       Date:  2016-05       Impact factor: 3.117

8.  Reflective afocal broadband adaptive optics scanning ophthalmoscope.

Authors:  Alfredo Dubra; Yusufu Sulai
Journal:  Biomed Opt Express       Date:  2011-05-27       Impact factor: 3.732

9.  Noninvasive imaging of the human rod photoreceptor mosaic using a confocal adaptive optics scanning ophthalmoscope.

Authors:  Alfredo Dubra; Yusufu Sulai; Jennifer L Norris; Robert F Cooper; Adam M Dubis; David R Williams; Joseph Carroll
Journal:  Biomed Opt Express       Date:  2011-06-08       Impact factor: 3.732

Review 10.  Photoreceptor-Based Biomarkers in AOSLO Retinal Imaging.

Authors:  Katie M Litts; Robert F Cooper; Jacque L Duncan; Joseph Carroll
Journal:  Invest Ophthalmol Vis Sci       Date:  2017-05-01       Impact factor: 4.799

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  11 in total

1.  SPATIALLY INFORMED CNN FOR AUTOMATED CONE DETECTION IN ADAPTIVE OPTICS RETINAL IMAGES.

Authors:  Heng Jin; Jessica I W Morgan; James C Gee; Min Chen
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2020-05-22

2.  Automatic longitudinal montaging of adaptive optics retinal images using constellation matching.

Authors:  Min Chen; Robert F Cooper; James C Gee; David H Brainard; Jessica I W Morgan
Journal:  Biomed Opt Express       Date:  2019-11-25       Impact factor: 3.732

3.  Intergrader agreement of foveal cone topography measured using adaptive optics scanning light ophthalmoscopy.

Authors:  Niamh Wynne; Jenna A Cava; Mina Gaffney; Heather Heitkotter; Abigail Scheidt; Jenny L Reiniger; Jenna Grieshop; Kai Yang; Wolf M Harmening; Robert F Cooper; Joseph Carroll
Journal:  Biomed Opt Express       Date:  2022-08-01       Impact factor: 3.562

4.  Comparison of Cone Mosaic Metrics From Images Acquired With the SPECTRALIS High Magnification Module and Adaptive Optics Scanning Light Ophthalmoscopy.

Authors:  Niamh Wynne; Heather Heitkotter; Erica N Woertz; Robert F Cooper; Joseph Carroll
Journal:  Transl Vis Sci Technol       Date:  2022-05-02       Impact factor: 3.048

5.  ENHANCED S-CONE SYNDROME: VISUAL FUNCTION, CROSS-SECTIONAL IMAGING, AND CELLULAR STRUCTURE WITH ADAPTIVE OPTICS OPHTHALMOSCOPY.

Authors:  Michael J Ammar; Kurt T Scavelli; Katherine E Uyhazi; Emma C Bedoukian; Leona W Serrano; Ilaina D Edelstein; Grace Vergilio; Robert F Cooper; Jessica I W Morgan; Priyanka Kumar; Tomas S Aleman
Journal:  Retin Cases Brief Rep       Date:  2021-11-01

Review 6.  Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO).

Authors:  Niamh Wynne; Joseph Carroll; Jacque L Duncan
Journal:  Prog Retin Eye Res       Date:  2020-11-06       Impact factor: 19.704

7.  Residual Cone Structure in Patients With X-Linked Cone Opsin Mutations.

Authors:  Emily J Patterson; Angelos Kalitzeos; Melissa Kasilian; Jessica C Gardner; Jay Neitz; Alison J Hardcastle; Maureen Neitz; Joseph Carroll; Michel Michaelides
Journal:  Invest Ophthalmol Vis Sci       Date:  2018-08-01       Impact factor: 4.799

8.  Interocular symmetry, intraobserver repeatability, and interobserver reliability of cone density measurements in the 13-lined ground squirrel.

Authors:  Benjamin S Sajdak; Alexander E Salmon; Rachel E Linderman; Jenna A Cava; Heather Heitkotter; Joseph Carroll
Journal:  PLoS One       Date:  2019-09-26       Impact factor: 3.240

9.  Cone Identification in Choroideremia: Repeatability, Reliability, and Automation Through Use of a Convolutional Neural Network.

Authors:  Jessica I W Morgan; Min Chen; Andrew M Huang; Yu You Jiang; Robert F Cooper
Journal:  Transl Vis Sci Technol       Date:  2020-07-16       Impact factor: 3.283

10.  Emulated retinal image capture (ERICA) to test, train and validate processing of retinal images.

Authors:  Laura K Young; Hannah E Smithson
Journal:  Sci Rep       Date:  2021-05-27       Impact factor: 4.379

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