ArticleBiomedical optics express2019
Layer boundary evolution method for macular OCT layer segmentation.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- VALIDATION OF A DEEP LEARNING-BASED ALGORITHM FOR SEGMENTATION OF THE ELLIPSOID ZONE ON OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AN USH2A-RELATED RETINAL DEGENERATION CLINICAL TRIAL.Retina (Philadelphia, Pa.) · 2022Trial
- Emerging Oculomic Signatures: Linking Thickness of Entire Retinal Layers with Plasma Biomarkers in Preclinical Alzheimer's Disease.Journal of clinical medicine · 2025Article
- Deep learning network with differentiable dynamic programming for retina OCT surface segmentation.Biomedical optics express · 2023Article
- Longitudinal deep network for consistent OCT layer segmentation.Biomedical optics express · 2023Article
- Artificial intelligence method based on multi-feature fusion for automatic macular edema (ME) classification on spectral-domain optical coherence tomography (SD-OCT) images.Frontiers in neuroscience · 2023Article
- Structured layer surface segmentation for retina OCT using fully convolutional regression networks.Medical image analysis · 2021Article
- Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra- and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.