Literature DB >> 28858152

Prevalence and Associated Factors of Segmentation Errors in the Peripapillary Retinal Nerve Fiber Layer and Macular Ganglion Cell Complex in Spectral-domain Optical Coherence Tomography Images.

Atsuya Miki1, Miho Kumoi, Shinichi Usui, Takao Endo, Rumi Kawashima, Takeshi Morimoto, Kenji Matsushita, Takashi Fujikado, Kohji Nishida.   

Abstract

PURPOSE: To determine the prevalence of errors in segmentation of the peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell complex (GCC) boundary in spectral-domain optical coherence tomography (SDOCT) images, and to identify factors associated with the errors.
MATERIALS AND METHODS: Peripapillary RNFL circle scans and macular 3-dimensional scans of consecutive cases imaged with SDOCT (RS-3000 Advance; Nidek, Gamagori, Japan) were retrospectively reviewed by a glaucoma specialist. Images with signal strength index (SSI)<6 were excluded. Threshold for segmentation failure was determined as 15 degrees in the RNFL scans and 1/24 of the scanned area in the GCC scans. Relationships between segmentation failure and clinical factors were statistically evaluated with univariable and multivariable analyses.
RESULTS: This retrospective cross-sectional study included 207 eyes of 117 subjects (mean age, 58.5±16.5 y). Segmentation failure was found in 20.7% of the peripapillary RNFL scans, 16.6% of the 9 mm GCC scans, and 6.9% of the 6 mm GCC scans in SDOCT images. In multivariable logistic regression analyses, low SSI, large disc area, and disease type significantly correlated with RNFL segmentation failure, whereas SSI was the only baseline factor that was significantly associated with GCC segmentation failure.
CONCLUSIONS: Although segmentation failure was common in both RNFL and GCC scans, it was less frequently observed in GCC scans. SSI, disc area, and disease type were significantly associated with segmentation failure. Predictive performance of baseline factors for failure was poor, underlining the importance of reviewing raw OCT images before using OCT parameters.

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Mesh:

Year:  2017        PMID: 28858152     DOI: 10.1097/IJG.0000000000000771

Source DB:  PubMed          Journal:  J Glaucoma        ISSN: 1057-0829            Impact factor:   2.503


  10 in total

1.  Data-Driven, Feature-Agnostic Deep Learning vs Retinal Nerve Fiber Layer Thickness for the Diagnosis of Glaucoma.

Authors:  Christine A Petersen; Parmita Mehta; Aaron Y Lee
Journal:  JAMA Ophthalmol       Date:  2020-04-01       Impact factor: 7.389

2.  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

3.  Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images.

Authors:  Parmita Mehta; Christine A Petersen; Joanne C Wen; Michael R Banitt; Philip P Chen; Karine D Bojikian; Catherine Egan; Su-In Lee; Magdalena Balazinska; Aaron Y Lee; Ariel Rokem
Journal:  Am J Ophthalmol       Date:  2021-05-02       Impact factor: 5.258

4.  Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets.

Authors:  Erfan Noury; Suria S Mannil; Robert T Chang; An Ran Ran; Carol Y Cheung; Suman S Thapa; Harsha L Rao; Srilakshmi Dasari; Mohammed Riyazuddin; Dolly Chang; Sriharsha Nagaraj; Clement C Tham; Reza Zadeh
Journal:  Transl Vis Sci Technol       Date:  2022-05-02       Impact factor: 3.048

5.  Evaluation of Structure-Function Relationships in Longitudinal Changes of Glaucoma using the Spectralis OCT Follow-Up Mode.

Authors:  Kenji Suda; Tadamichi Akagi; Hideo Nakanishi; Hisashi Noma; Hanako Ohashi Ikeda; Takanori Kameda; Tomoko Hasegawa; Akitaka Tsujikawa
Journal:  Sci Rep       Date:  2018-11-21       Impact factor: 4.379

6.  Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm.

Authors:  Alessandro A Jammal; Atalie C Thompson; Nara G Ogata; Eduardo B Mariottoni; Carla N Urata; Vital P Costa; Felipe A Medeiros
Journal:  Sci Rep       Date:  2019-07-08       Impact factor: 4.379

7.  Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis.

Authors:  Ghazale Razaghi; Ehsan Hedayati; Marjaneh Hejazi; Rahele Kafieh; Melika Samadi; Robert Ritch; Prem S Subramanian; Masoud Aghsaei Fard
Journal:  Sci Rep       Date:  2022-10-12       Impact factor: 4.996

Review 8.  A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression.

Authors:  Atalie C Thompson; Alessandro A Jammal; Felipe A Medeiros
Journal:  Transl Vis Sci Technol       Date:  2020-07-22       Impact factor: 3.283

9.  Reasons why OCT Global Circumpapillary Retinal Nerve Fiber Layer Thickness is a Poor Measure of Glaucomatous Progression.

Authors:  Melvi D Eguia; Emmanouil Tsamis; Zane Z Zemborain; Ashley Sun; Joseph Percival; C Gustavo De Moraes; Robert Ritch; Donald C Hood
Journal:  Transl Vis Sci Technol       Date:  2020-10-19       Impact factor: 3.283

10.  Artifact Rates for 2D Retinal Nerve Fiber Layer Thickness Versus 3D Neuroretinal Rim Thickness Using Spectral-Domain Optical Coherence Tomography.

Authors:  Elli A Park; Edem Tsikata; Jenny Jyoung Lee; Eric Shieh; Boy Braaf; Benjamin J Vakoc; Brett E Bouma; Johannes F de Boer; Teresa C Chen
Journal:  Transl Vis Sci Technol       Date:  2020-09-10       Impact factor: 3.283

  10 in total

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