ArticlePloS one2019
Validation of automated artificial intelligence segmentation of optical coherence tomography images.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 76 citations in OpenAlex.
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- AI-based analysis of surgical outcomes in vitrectomy with and without cystotomy for refractory cystoid diabetic macular edema.Scientific reports · 2025Article
- Photoreceptor assessment in age-related macular degeneration.Eye (London, England) · 2025Review
- Factors influencing image quality in Tibetan children by optical coherence tomography.Frontiers in medicine · 2025Article
- Frequency tagged multifocal pupillary response fields identify age-related macular degeneration and diabetic retinopathy.Frontiers in neuroscience · 2025Article
- Band Visibility in High-Resolution Optical Coherence Tomography Assessed With a Custom Review Tool and Updated, Histology-Derived Nomenclature.Translational vision science & technology · 2024Article
- Why we should not mistake accuracy of medical AI for efficiency.NPJ digital medicine · 2024Article
- A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray.Nature communications · 2024Article
- IoT System Based on Artificial Intelligence for Hot Spot Detection in Photovoltaic Modules for a Wide Range of Irradiances.Sensors (Basel, Switzerland) · 2023Article
- Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.Ophthalmology science · 2023Article
- Automated Detection of Posterior Vitreous Detachment on OCT Using Computer Vision and Deep Learning Algorithms.Ophthalmology science · 2023Article
- The Classification of Common Macular Diseases Using Deep Learning on Optical Coherence Tomography Images with and without Prior Automated Segmentation.Diagnostics (Basel, Switzerland) · 2023Article
- Reticular Pseudodrusen Are Associated With More Advanced Para-Central Photoreceptor Degeneration in Intermediate Age-Related Macular Degeneration.Investigative ophthalmology & visual science · 2022Article
- Evolution and Applications of Artificial Intelligence to Cataract Surgery.Ophthalmology science · 2022Article
- Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning.Translational vision science & technology · 2022Article
- Clinically applicable deep learning-based decision aids for treatment of neovascular AMD.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
- High-Density Optical Coherence Tomography Analysis Provides Insights Into Early/Intermediate Age-Related Macular Degeneration Retinal Layer Changes.Investigative ophthalmology & visual science · 2022Article
- Choroidal imaging using optical coherence tomography: techniques and interpretations.Japanese journal of ophthalmology · 2022Review
- From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration.Ophthalmology · 2022Review
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera.
methodsA convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results.
resultsThe CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison.
conclusionThe proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders.
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