ArticleTranslational vision science & technology2016
Automated Segmentability Index for Layer Segmentation of Macular SD-OCT Images.
Article in Translational vision science & technology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Total retinal thickness is an important factor in evaluating diabetic retinal neurodegeneration.BMJ open ophthalmology · 2024Article
- Reduced Thickness of the Retina in de novo Parkinson's Disease Shows A Distinct Pattern, Different from Glaucoma.Journal of Parkinson's disease · 2024Article
- Review
- Total retinal thickness: a neglected factor in the evaluation of inner retinal thickness.BMJ open ophthalmology · 2022Article
- Livelayer: a semi-automatic software program for segmentation of layers and diabetic macular edema in optical coherence tomography images.Scientific reports · 2021Article
- Detecting retinal neurodegeneration in people with diabetes: Findings from the UK Biobank.PloS one · 2021Article
- [Potential of methods of artificial intelligence for quality assurance].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2020Review
- Thinner retinal layers are associated with changes in the visual pathway: A population-based study.Human brain mapping · 2018Article
- Association of Retinal Neurodegeneration on Optical Coherence Tomography With Dementia: A Population-Based Study.JAMA neurology · 2018Article
- Automated drusen detection in dry age-related macular degeneration by multiple-depth,Biomedical optics express · 2017Article
- Optical Coherence Tomography Analysis Based Prediction of Humphrey 24-2 Visual Field Thresholds in Patients With Glaucoma.Investigative ophthalmology & visual science · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeTo automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies.
methodsSix hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm
resultsThe 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC (
conclusionsThe segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected. TRANSLATIONAL RELEVANCE: Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies.
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