ArticleJournal of diabetes research2021
Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net.
Article in Journal of diabetes research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- BigEye: a clinically interpretable deep learning framework for diabetic retinopathy detection and stage prediction.Scientific reports · 2026Article
- Automated quantification of retinal non-perfusion areas in ultra-widefield SS-OCTA image of patient with retinal vascular diseases by NP-Unet model.BMJ open ophthalmology · 2025Article
- Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.Translational vision science & technology · 2024Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a prevalent vision-threatening disease worldwide. Laser marks are the scars left after panretinal photocoagulation, a treatment to prevent patients with severe DR from losing vision. In this study, we develop a deep learning algorithm based on the lightweight U-Net to segment laser marks from the color fundus photos, which could help indicate a stage or providing valuable auxiliary information for the care of DR patients. We prepared our training and testing data, manually annotated by trained and experienced graders from Image Reading Center, Zhongshan Ophthalmic Center, publicly available to fill the vacancy of public image datasets dedicated to the segmentation of laser marks. The lightweight U-Net, along with two postprocessing procedures, achieved an AUC of 0.9824, an optimal sensitivity of 94.16%, and an optimal specificity of 92.82% on the segmentation of laser marks in fundus photographs. With accurate segmentation and high numeric metrics, the lightweight U-Net method showed its reliable performance in automatically segmenting laser marks in fundus photographs, which could help the AI assist the diagnosis of DR in the severe stage.
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Registered trials
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