Literature DB >> 26132774

Impact of segmentation errors and retinal blood vessels on retinal nerve fibre layer measurements using spectral-domain optical coherence tomography.

Cong Ye1, Marco Yu1,2, Christopher Kai-Shun Leung1.   

Abstract

PURPOSE: To investigate the impact of retinal blood vessels and retinal nerve fibre layer (RNFL) segmentation errors on RNFL measurement.
METHODS: One eye of 180 subjects (60 normal, 66 mild-to-moderate and 54 advanced glaucoma subjects) was randomly selected for RNFL imaging with a spectral-domain OCT. The boundaries of the RNFL detected by the instrument software were checked, and the segmentation errors were corrected by a customized computer program. The differences in average and regional RNFL thicknesses (RNFLT) before and after the correction were analysed to determine the frequency of segmentation error (defined as an absolute difference in average RNFLT >5.0 μm). The ratio of retinal blood vessel cross-sectional area to RNFL cross-sectional area was calculated.
RESULTS: The difference in average RNFLT (postsegmentation minus presegmentation refinement) ranged from -3.0 to 2.5 μm (mean ± standard deviation: 0.83 ± 0.86 μm) in the normal, -2.5 to 5.0 μm (0.56 ± 1.08 μm) in the mild-to-moderate glaucoma and -11.0 to 9.5 μm (0.05 ± 3.49 μm) in the advanced glaucoma groups (p = 0.003). A total of 15% of eyes had average RNFLT measurement error >5.0 μm in the advanced glaucoma group. The proportion of retinal blood vessels in the RNFL also increased with the severity of glaucoma (p < 0.001) with 4.2 ± 1.0% in the normal group, 4.9 ± 1.5% in the mild-to-moderate and 8.5 ± 3.5% in the advanced glaucoma groups.
CONCLUSIONS: Inclusion of retinal blood vessels and RNFL segmentation error could adversely affect RNFL measurement, particularly in advanced glaucoma when the RNFL was thin.
© 2015 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  glaucoma; image analysis; optical coherence tomography; retinal blood vessels; retinal nerve fibre layer; segmentation

Mesh:

Year:  2015        PMID: 26132774     DOI: 10.1111/aos.12762

Source DB:  PubMed          Journal:  Acta Ophthalmol        ISSN: 1755-375X            Impact factor:   3.761


  21 in total

1.  DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images.

Authors:  Sripad Krishna Devalla; Prajwal K Renukanand; Bharathwaj K Sreedhar; Giridhar Subramanian; Liang Zhang; Shamira Perera; Jean-Martial Mari; Khai Sing Chin; Tin A Tun; Nicholas G Strouthidis; Tin Aung; Alexandre H Thiéry; Michaël J A Girard
Journal:  Biomed Opt Express       Date:  2018-06-25       Impact factor: 3.732

2.  Segregation of neuronal-vascular components in a retinal nerve fiber layer for thickness measurement using OCT and OCT angiography.

Authors:  Ai Ping Yow; Bingyao Tan; Jacqueline Chua; Rahat Husain; Leopold Schmetterer; Damon Wong
Journal:  Biomed Opt Express       Date:  2021-05-07       Impact factor: 3.732

3.  Optical Coherence Tomography Segmentation Errors of the Retinal Nerve Fiber Layer Persist Over Time.

Authors:  Nisha Nagarkatti-Gude; Stuart K Gardiner; Brad Fortune; Shaban Demirel; Steven L Mansberger
Journal:  J Glaucoma       Date:  2019-05       Impact factor: 2.503

4.  Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma.

Authors:  Steven L Mansberger; Shivali A Menda; Brad A Fortune; Stuart K Gardiner; Shaban Demirel
Journal:  Am J Ophthalmol       Date:  2016-11-04       Impact factor: 5.258

5.  Estimating Optical Coherence Tomography Structural Measurement Floors to Improve Detection of Progression in Advanced Glaucoma.

Authors:  Christopher Bowd; Linda M Zangwill; Robert N Weinreb; Felipe A Medeiros; Akram Belghith
Journal:  Am J Ophthalmol       Date:  2016-11-30       Impact factor: 5.258

6.  Rationale and Development of an OCT-Based Method for Detection of Glaucomatous Optic Neuropathy.

Authors:  Jeffrey M Liebmann; Donald C Hood; Carlos Gustavo de Moraes; Dana M Blumberg; Noga Harizman; Yocheved S Kresch; Emmanouil Tsamis; George A Cioffi
Journal:  J Glaucoma       Date:  2022-02-28       Impact factor: 2.290

7.  Protruded retinal layers within the optic nerve head neuroretinal rim.

Authors:  Lucas A Torres; Jayme R Vianna; Faisal Jarrar; Glen P Sharpe; Makoto Araie; Joseph Caprioli; Shaban Demirel; Christopher A Girkin; Masanori Hangai; Aiko Iwase; Jeffrey M Liebmann; Christian Y Mardin; Toru Nakazawa; Harry A Quigley; Alexander F Scheuerle; Kazuhisa Sugiyama; Hidenobu Tanihara; Goji Tomita; Yasuo Yanagi; Claude F Burgoyne; Balwantray C Chauhan
Journal:  Acta Ophthalmol       Date:  2018-06       Impact factor: 3.761

8.  Detecting Progression in Advanced Glaucoma: Are Optical Coherence Tomography Global Metrics Viable Measures?

Authors:  Abinaya Thenappan; Emmanouil Tsamis; Zane Z Zemborain; Sol La Bruna; Melvi Eguia; Devon Joiner; Carlos Gustavo De Moraes; Donald C Hood
Journal:  Optom Vis Sci       Date:  2021-05-01       Impact factor: 2.106

9.  A Case for the Use of Artificial Intelligence in Glaucoma Assessment.

Authors:  Joel S Schuman; Maria De Los Angeles Ramos Cadena; Rebecca McGee; Lama A Al-Aswad; Felipe A Medeiros
Journal:  Ophthalmol Glaucoma       Date:  2021-12-22

10.  Structural Change Can Be Detected in Advanced-Glaucoma Eyes.

Authors:  Akram Belghith; Felipe A Medeiros; Christopher Bowd; Jeffrey M Liebmann; Christopher A Girkin; Robert N Weinreb; Linda M Zangwill
Journal:  Invest Ophthalmol Vis Sci       Date:  2016-07-01       Impact factor: 4.799

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