ArticleTranslational vision science & technology2020
Fast and Automated Hyperreflective Foci Segmentation Based on Image Enhancement and Improved 3D U-Net in SD-OCT Volumes with Diabetic Retinopathy.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Long term evolutions of hard exudates after anti-VEGF therapy for diabetic macular oedema.Eye (London, England) · 2026Trial
- Hard exudates in diabetic macular edema after intravitreal anti-VEGF therapy: a post-hoc analysis of the DRCR protocol T trial.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Trial
- U-Net Optimization for Hyperreflective Foci Segmentation in Retinal OCT.Diagnostics (Basel, Switzerland) · 2026Article
- A new texture-based labeling framework for hyper-reflective foci identification in retinal optical coherence tomography images.Scientific reports · 2024Article
- Exploration on OCT biomarker candidate related to macular edema caused by diabetic retinopathy and retinal vein occlusion in SD-OCT images.Scientific reports · 2024Article
- Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble.PloS one · 2024Article
- Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research.International journal of ophthalmology · 2024Review
- Diabetic Macular Edema Optical Coherence Tomography Biomarkers Detected with EfficientNetV2B1 and ConvNeXt.Diagnostics (Basel, Switzerland) · 2023Article
- Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Segmentation of Hyperreflective Foci in OCT Images Based on Lightweight DBR Network.Journal of digital imaging · 2023Article
- Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection.Frontiers in medicine · 2023Article
- Retinal optical coherence tomography image analysis by a restricted Boltzmann machine.Biomedical optics express · 2022Article
- Depth-resolved visualization and automated quantification of hyperreflective foci on OCT scans using optical attenuation coefficients.Biomedical optics express · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To design a robust and automated hyperreflective foci (HRF) segmentation framework for spectral-domain optical coherence tomography (SD-OCT) volumes, especially volumes with low HRF-background contrast. Methods: HRF in retinal SD-OCT volumes appear with low-contrast characteristics that results in the difficulty of HRF segmentation. Therefore to effectively segment the HRF we proposed a fully automated method for HRF segmentation in SD-OCT volumes with diabetic retinopathy (DR). First, we generated the enhanced SD-OCT images from the denoised SD-OCT images with an enhancement method. Then the enhanced images were cascaded with the denoised images as the two-channel input to the network against the low-contrast HRF. Finally, we replaced the standard convolution with slice-wise dilated convolution in the last layer of the encoder path of 3D U-Net to obtain long-range information. Results: We evaluated our method using two-fold cross-validation on 33 SD-OCT volumes from 27 patients. The average dice similarity coefficient was 70.73%, which was higher than that of the existing methods with significant difference ( Conclusions: Experimental results demonstrated that the proposed method is faster and achieves more reliable segmentation results than the current HRF segmentation algorithms. We expect that this method will contribute to clinical diagnosis and disease surveillance. Translational Relevance: Our framework for the automated HRF segmentation of SD-OCT volumes may improve the clinical diagnosis of DR.
Indexed as
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.