ArticlePloS one2016
Prevalence and Distribution of Segmentation Errors in Macular Ganglion Cell Analysis of Healthy Eyes Using Cirrus HD-OCT.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Influence of OSCAR-IB Criteria on Test-Retest Reliability of Cirrus HD-OCT Retinal Thickness Measurements in People With Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2025Article
- Idiopathic Macular Hole Area to Foveal Avascular Zone Ratio and Its Effects on Visual Acuity Before and After Surgery.Translational vision science & technology · 2025Article
- Utility of ganglion cells for the evaluation of anterior visual pathway pathology: a review.Acta neurologica Belgica · 2024Review
- Detection of Longitudinal Ganglion Cell/Inner Plexiform Layer Change: Comparison of Two Spectral-Domain Optical Coherence Tomography Devices.American journal of ophthalmology · 2021Article
- Comparison between two multimodal imaging platforms: Nidek Mirante and Heidelberg Spectralis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2021Article
- Extended Ganglion Cell Layer Thickness Deviation Maps With OCT in Glaucoma Diagnosis.Frontiers in medicine · 2021Article
- Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning.Biomedical optics express · 2020Article
- A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head.Scientific reports · 2019Article
- Performance of Topcon 3D optical coherence tomography-2000 in re-analyzing OCT-1000 raw data.Experimental and therapeutic medicine · 2019Article
- Optical Coherence Tomography Segmentation Errors of the Retinal Nerve Fiber Layer Persist Over Time.Journal of glaucoma · 2019Article
- QUANTITATIVE ANALYSIS OF THE INNER RETINAL LAYER THICKNESSES IN AGE-RELATED MACULAR DEGENERATION USING CORRECTED OPTICAL COHERENCE TOMOGRAPHY SEGMENTATION.Retina (Philadelphia, Pa.) · 2018Article
- DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images.Biomedical optics express · 2018Article
- Wide scan imaging with swept-source optical coherent tomography for glaucoma diagnosis.PloS one · 2018Article
- Automated Segmentation Errors When Using Optical Coherence Tomography to Measure Retinal Nerve Fiber Layer Thickness in Glaucoma.American journal of ophthalmology · 2017Article
- Segmentation errors in macular ganglion cell analysis as determined by optical coherence tomography in eyes with macular pathology.International journal of retina and vitreous · 2017Article
- Article
- Stepwise segmentation error correction in optical coherence tomography angiography images of patients with diabetic macular edema.Therapeutic advances in ophthalmologyArticle
- Artifacts in Macular Optical Coherence Tomography.Journal of current ophthalmologyReview
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo determine the frequency of different types of spectral domain optical coherence tomography (SD-OCT) scan artifacts and errors in ganglion cell algorithm (GCA) in healthy eyes.
methodsInfrared image, color-coded map and each of the 128 horizontal b-scans acquired in the macular ganglion cell-inner plexiform layer scans using the Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA) macular cube 512 × 128 protocol in 30 healthy normal eyes were evaluated. The frequency and pattern of each artifact was determined. Deviation of the segmentation line was classified into mild (less than 10 microns), moderate (10-50 microns) and severe (more than 50 microns). Each deviation, if present, was noted as upward or downward deviation. Each artifact was further described as per location on the scan and zones in the total scan area.
resultsA total of 1029 (26.8%) out of total 3840 scans had scan errors. The most common scan error was segmentation error (100%), followed by degraded images (6.70%), blink artifacts (0.09%) and out of register artifacts (3.3%). Misidentification of the inner retinal layers was most frequent (62%). Upward Deviation of the segmentation line (47.91%) and severe deviation (40.3%) were more often noted. Artifacts were mostly located in the central scan area (16.8%). The average number of scans with artifacts per eye was 34.3% and was not related to signal strength on Spearman correlation (p = 0.36).
conclusionsThis study reveals that image artifacts and scan errors in SD-OCT GCA analysis are common and frequently involve segmentation errors. These errors may affect inner retinal thickness measurements in a clinically significant manner. Careful review of scans for artifacts is important when using this feature of SD-OCT device.
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