ArticleBiomedical optics express2024
Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning.
Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Adaptive Temporal Mixture of Experts for Predicting Stiffness Metrics From the Ocular Response Analyzer and Identifying Keratoconus.American journal of ophthalmology · 2026Observational
- ISOSNet: a unified framework for cone photoreceptor detection and inner segment and outer segment length measurement from AO-OCT B-scans.Biomedical optics express · 2025Article
- Review
- Identifying retinal pigment epithelium cells in adaptive optics-optical coherence tomography images with partial annotations and superhuman accuracy.Biomedical optics express · 2024Article
- Reducing manual labeling requirements and improved retinal ganglion cell identification in 3D AO-OCT volumes using semi-supervised learning.Biomedical optics express · 2024Article
- Identifying retinopathy in optical coherence tomography images with less labeled data via contrastive graph regularization.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Adaptive optics-optical coherence tomography (AO-OCT) allows for the three-dimensional visualization of retinal ganglion cells (RGCs) in the living human eye. Quantitative analyses of RGCs have significant potential for improving the diagnosis and monitoring of diseases such as glaucoma. Recent advances in machine learning (ML) have made possible the automatic identification and analysis of RGCs within the complex three-dimensional retinal volumes obtained with such imaging. However, the current state-of-the-art ML approach relies on fully supervised training, which demands large amounts of training labels. Each volume requires many hours of expert manual annotation. Here, two semi-supervised training schemes are introduced, (i) cross-consistency training and (ii) cross pseudo supervision that utilize unlabeled AO-OCT volumes together with a minimal set of labels, vastly reducing the labeling demands. Moreover, these methods outperformed their fully supervised counterpart and achieved accuracy comparable to that of human experts.
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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.