ArticleOphthalmology science2026
Automated Feature Segmentation of Ultra-Widefield OCT Images.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Multi-modal and inter-device retinal OCT registration via learning-based feature correspondence.Biomedical optics express · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Objective or Purpose: To develop a lightweight neural network for automated cross-sectional and en face segmentation of ultra-widefield (UWF) OCT images acquired for retinopathy of prematurity screening. Design: Cross-sectional study. Subjects: Twenty-five infants with a birth weight <1500 g or gestational age <31 weeks were scanned using a portable, handheld, swept-source UWF-OCT device. Methods Intervention or Testing: For cross-sectional B-scans, 3040 B-scans from 5 OCT volumetric scans obtained from 5 patients were segmented by 2 graders for the choroid and retina using custom-built tools in the Napari image viewer. Using these segmentations, a u-net with an EfficientNet-B0 backbone was trained in combination with task-specific augmentations to perform automated segmentation of the retina and choroid data with varying levels of image processing applied. For en face scans, 40 en face images from 20 unique patients were manually segmented by a single grader for retinal vessels. Using these segmentations, a u-net with an EfficientNet-B0 backbone was trained. Validation for both B-scans and en face images was performed using fivefold cross-validation. The fivefold cross-validation metrics were then compared with the metrics obtained by comparing grader segmentations. Main Outcome Measures: The Dice similarity coefficient (DSC) was used to assess B-scan and en face segmentations. Results: The retinal and choroidal b-scan segmentations produced a DSC ± standard deviation of 0.925 ± 0.021 and 0.797 ± 0.062, respectively, averaged across the fivefolds. The en face vasculature segmentation produced a DSC ± standard deviation of 0.625 ± 0.0450. Conclusions: Using u-net convolutional neural networks trained with task-specific augmentations, we developed en face and cross-sectional segmentations for UWF-OCT images, which will facilitate automated quantitative analysis with this novel modality. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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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 Socratic graph.