ArticleBiomedical optics express2020
Semi-supervised deep learning based 3D analysis of the peripapillary region.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- Comparative Evaluation of Deep Generative Models for Predicting 12-Month Neovascular AMD Progression Using OCT and Fundus Photography.Journal of imaging informatics in medicine · 2026Article
- Deep learning method for semi-automated segmentation of optic nerve head tissues in optical coherence tomography images.Experimental eye research · 2025Article
- Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review.NMR in biomedicine · 2025Review
- Loss values of style transfer from UBM to AS-OCT images for plateau iris classification.Scientific reports · 2024Article
- Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.Acta neuropathologica communications · 2024Article
- Cost-efficient and glaucoma-specifical model by exploiting normal OCT images with knowledge transfer learning.Biomedical optics express · 2023Article
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
- Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.JAMA ophthalmology · 2022Article
- Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey.Eye and vision (London, England) · 2022Review
- Prediction of corneal astigmatism based on corneal tomography after femtosecond laser arcuate keratotomy using a pix2pix conditional generative adversarial network.Frontiers in public health · 2022Article
- Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images.Biomedical optics express · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical coherence tomography (OCT) has become an essential tool in the evaluation of glaucoma, typically through analyzing retinal nerve fiber layer changes in circumpapillary scans. Three-dimensional OCT volumes enable a much more thorough analysis of the optic nerve head (ONH) region, which may be the site of initial glaucomatous optic nerve damage. Automated analysis of this region is of great interest, though large anatomical variations and the termination of layers make the requisite peripapillary layer and Bruch's membrane opening (BMO) segmentation a challenging task. Several machine learning-based segmentation methods have been proposed for retinal layer segmentation, and a few for the ONH region, but they typically depend on either heavily averaged or pre-processed B-scans or a large amount of annotated data, which is a tedious task and resource-intensive. We evaluated a semi-supervised adversarial deep learning method for segmenting peripapillary retinal layers in OCT B-scans to take advantage of unlabeled data. We show that the use of a generative adversarial network and unlabeled data can improve the performance of segmentation. Additionally, we use a Faster R-CNN architecture to automatically segment the BMO. The proposed methods are then used for the 3D morphometric analysis of both control and glaucomatous ONH volumes to demonstrate the potential for clinical utility.
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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.