ArticleScientific reports2022
Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- SWAU-Net: Longitudinal Prediction of Geographic Atrophy via Sliding-Window Attention.Life (Basel, Switzerland) · 2026Article
- Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping.International ophthalmology · 2025Review
- Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy.medRxiv : the preprint server for health sciences · 2024Article
- High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention.Biomedical optics express · 2024Article
- Using Ensemble OCT-Derived Features beyond Intensity Features for Enhanced Stargardt Atrophy Prediction with Deep Learning.Applied sciences (Basel, Switzerland) · 2023Article
- American Society of Retina Specialists Clinical Practice Guidelines on Multimodal Imaging for Retinal Disease.Journal of vitreoretinal diseasesArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Age-related macular degeneration (AMD) and Stargardt disease are the leading causes of blindness for the elderly and young adults respectively. Geographic atrophy (GA) of AMD and Stargardt atrophy are their end-stage outcomes. Efficient methods for segmentation and quantification of these atrophic lesions are critical for clinical research. In this study, we developed a deep convolutional neural network (CNN) with a trainable self-attended mechanism for accurate GA and Stargardt atrophy segmentation. Compared with traditional post-hoc attention mechanisms which can only visualize CNN features, our self-attended mechanism is embedded in a fully convolutional network and directly involved in training the CNN to actively attend key features for enhanced algorithm performance. We applied the self-attended CNN on the segmentation of AMD and Stargardt atrophic lesions on fundus autofluorescence (FAF) images. Compared with a preexisting regular fully convolutional network (the U-Net), our self-attended CNN achieved 10.6% higher Dice coefficient and 17% higher IoU (intersection over union) for AMD GA segmentation, and a 22% higher Dice coefficient and a 32% higher IoU for Stargardt atrophy segmentation. With longitudinal image data having over a longer time, the developed self-attended mechanism can also be applied on the visual discovery of early AMD and Stargardt features.
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