ArticleBiomedical optics express2020
Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning.
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 18 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- Review
- Robustness of automated Bruch's membrane opening minimum rim width and retinal nerve fiber layer thickness without clinical disc margin adjustment in glaucoma.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- The Application of a Deep Learning Algorithm for the Segmentation of Retinal Nerve Fiber Layer Across Different Optic Neuropathies.Translational vision science & technology · 2026Article
- An automated optical coherence tomography to finite element analysis pipeline reveals key morphological determinants of optic nerve head biomechanics in glaucoma.Eye (London, England) · 2026Article
- 3D Structural Phenotype of the Optic Nerve Head in Glaucoma and Myopia-A Key to Improving Glaucoma Diagnosis in Myopic Populations.American journal of ophthalmology · 2026Article
- Deep learning method for semi-automated segmentation of optic nerve head tissues in optical coherence tomography images.Experimental eye research · 2025Article
- Are Macula or Optic Nerve Head Structures Better at Diagnosing Glaucoma? An Answer Using Artificial Intelligence and Wide-Field Optical Coherence Tomography.Translational vision science & technology · 2024Article
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Deep learning segmentation of the tear fluid reservoir under the sclera lens in optical coherence tomography images.Biomedical optics express · 2023Article
- Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma.Translational vision science & technology · 2023Article
- Quantitative optical coherence microscopy of neuron morphology in human entorhinal cortex.Frontiers in neuroscience · 2023Article
- Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications.Frontiers in ophthalmology · 2023Article
- Measurement of retinal nerve fiber layer thickness with a deep learning algorithm in ischemic optic neuropathy and optic neuritis.Scientific reports · 2022Article
- [Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data].Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft · 2021Review
- Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images.Biomedical optics express · 2021Article
- Semi-supervised deep learning based 3D analysis of the peripapillary region.Biomedical optics express · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 7 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently proposed deep learning (DL) algorithms for the segmentation of optical coherence tomography (OCT) images to quantify the morphological changes to the optic nerve head (ONH) tissues during glaucoma have limited clinical adoption due to their device specific nature and the difficulty in preparing manual segmentations (training data). We propose a DL-based 3D segmentation framework that is easily translatable across OCT devices in a label-free manner (i.e. without the need to manually re-segment data for each device). Specifically, we developed 2 sets of DL networks: the 'enhancer' (enhance OCT image quality and harmonize image characteristics from 3 devices) and the 'ONH-Net' (3D segmentation of 6 ONH tissues). We found that only when the 'enhancer' was used to preprocess the OCT images, the 'ONH-Net' trained on any of the 3 devices successfully segmented ONH tissues from the other two unseen devices with high performance (Dice coefficients > 0.92). We demonstrate that is possible to automatically segment OCT images from new devices without ever needing manual segmentation data from them.
Identifiers
What OpenQuestion holds
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.