ArticleOphthalmology. Retina2022
Automated Segmentation of Autofluorescence Lesions in Stargardt Disease.
Article in Ophthalmology. Retina, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Automated Quantification of Decreased Fundus Autofluorescence in Stargardt Disease UsingOphthalmology science · 2026Article
- Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping.International ophthalmology · 2025Review
- Review
- Local progression kinetics of macular atrophy in recessive Stargardt disease.Ophthalmic genetics · 2023Article
- Using Ensemble OCT-Derived Features beyond Intensity Features for Enhanced Stargardt Atrophy Prediction with Deep Learning.Applied sciences (Basel, Switzerland) · 2023Article
- Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of >3500 Patients with Inherited Retinal Disease from the United Kingdom.Ophthalmology scienceArticle
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo train a deep learning (DL) algorithm to perform fully automated semantic segmentation of multiple autofluorescence lesion types in Stargardt disease.
designCross-sectional study with retrospective imaging data. SUBJECTS: The study included 193 images from 193 eyes of 97 patients with Stargardt disease.
methodsFundus autofluorescence images obtained from patient visits between 2013 and 2020 were annotated with ground-truth labels. Model training and evaluation were performed using fivefold cross-validation. MAIN OUTCOMES MEASURES: Dice similarity coefficients, intraclass correlation coefficients, and Bland-Altman analyses comparing algorithm-predicted and grader-labeled segmentations.
resultsThe overall Dice similarity coefficient across all lesion classes was 0.78 (95% confidence interval [CI], 0.69-0.86). Dice coefficients were 0.90 (95% CI, 0.85-0.94) for areas of definitely decreased autofluorescence (DDAF), 0.55 (95% CI, 0.35-0.76) for areas of questionably decreased autofluorescence (QDAF), and 0.88 (95% CI, 0.73-1.00) for areas of abnormal background autofluorescence (ABAF). Intraclass correlation coefficients comparing the ground-truth and automated methods were 0.997 (95% CI, 0.996-0.998) for DDAF, 0.863 (95% CI, 0.823-0.895) for QDAF, and 0.974 (95% CI, 0.966-0.980) for ABAF.
conclusionsA DL algorithm performed accurate segmentation of autofluorescence lesions in Stargardt disease, demonstrating the feasibility of fully automated segmentation as an alternative to manual or semiautomated labeling methods.
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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.