ArticleJournal of digital imaging2023
Automatic Segmentation of Hyperreflective Foci in OCT Images Based on Lightweight DBR Network.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Radiomics-based fundus autofluorescence analysis in central serous chorioretinopathy-MICRoN report number twelve.Scientific reports · 2026Article
- Baseline OCT biomarkers associated with visual prognosis in cystoid macular edema secondary to noninfectious uveitis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- U-Net Optimization for Hyperreflective Foci Segmentation in Retinal OCT.Diagnostics (Basel, Switzerland) · 2026Article
- Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.Ophthalmology and therapy · 2025Review
- Global trends and hotspots of inflammation in diabetic retinopathy: a literature review and bibliometric analysis.Frontiers in medicine · 2025Review
- A new texture-based labeling framework for hyper-reflective foci identification in retinal optical coherence tomography 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
- Unleashing the power of optical attenuation coefficients to facilitate segmentation strategies in OCT imaging of age-related macular degeneration: perspective.Biomedical optics express · 2023Article
- Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hyperreflective foci (HF) reflects inflammatory responses for fundus diseases such as diabetic macular edema (DME), retina vein occlusion (RVO), and central serous chorioretinopathy (CSC). Shown as high contrast and reflectivity in optical coherence tomography (OCT) images, automatic segmentation of HF in OCT images is helpful for the prognosis of fundus diseases. Previous traditional methods were time-consuming and required high computing power. Hence, we proposed a lightweight network to segment HF (with a speed of 57 ms per OCT image, at least 150 ms faster than other methods). Our framework consists of two stages: an NLM filter and patch-based split to preprocess images and a lightweight DBR neural network to segment HF automatically. Experimental results from 3000 OCT images of 300 patients (100 DME,100 RVO, and 100 CSC) revealed that our method achieved HF segmentation successfully. The DBR network had the area under curves dice similarity coefficient (DSC) of 83.65%, 76.43%, and 82.20% in segmenting HF in DME, RVO, and CSC on the test cohort respectively. Our DBR network achieves at least 5% higher DSC than previous methods. HF in DME was more easily segmented compared with the other two types. In addition, our DBR network is universally applicable to clinical practice with the ability to segment HF in a wide range of fundus diseases.
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