ArticleScientific reports2023
A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation.Scientific reports · 2026Article
- Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.Scientific reports · 2026Article
- Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.Progress in retinal and eye research · 2026Review
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- Interpretable multi-model deep learning framework for automated four-class diagnosis of ocular toxoplasmosis using fundus imaging.Scientific reports · 2025Article
- Uncovering urban morphology and environmental interactions of small towns using self organizing maps in Qinba Mountains of Southern Shaanxi China.Scientific reports · 2025Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- A-scan sequence transformers for palpation with optical coherence elastography.Biomedical optics express · 2025Article
- Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2025Article
- Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.Translational vision science & technology · 2025Article
- Benchmarking diffusion models against state-of-the-art architectures for OCT fluid biomarker segmentation.PloS one · 2025Article
- Efficient diagnostic classification of diverse pathologies through contextual eye movement data analysis with a novel hybrid architecture.Scientific reports · 2024Article
- Self-supervised learning-enhanced deep learning method for identifying myopic maculopathy in high myopia patients.iScience · 2024Article
- Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs.Bioengineering (Basel, Switzerland) · 2023Article
- Discriminative, generative artificial intelligence, and foundation models in retina imaging.Taiwan journal of ophthalmologyReview
- Enhancing Organizing Pneumonia Diagnosis: A Novel Super-token Transformer Approach for Masson Body Segmentation.In vivo (Athens, Greece)Article
- Vision transformers: The next frontier for deep learning-based ophthalmic image analysis.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neovascular age-related macular degeneration (nAMD) is one of the major causes of irreversible blindness and is characterized by accumulations of different lesions inside the retina. AMD biomarkers enable experts to grade the AMD and could be used for therapy prognosis and individualized treatment decisions. In particular, intra-retinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelium detachment (PED) are prominent biomarkers for grading neovascular AMD. Spectral-domain optical coherence tomography (SD-OCT) revolutionized nAMD early diagnosis by providing cross-sectional images of the retina. Automatic segmentation and quantification of IRF, SRF, and PED in SD-OCT images can be extremely useful for clinical decision-making. Despite the excellent performance of convolutional neural network (CNN)-based methods, the task still presents some challenges due to relevant variations in the location, size, shape, and texture of the lesions. This work adopts a transformer-based method to automatically segment retinal lesion from SD-OCT images and qualitatively and quantitatively evaluate its performance against CNN-based methods. The method combines the efficient long-range feature extraction and aggregation capabilities of Vision Transformers with data-efficient training of CNNs. The proposed method was tested on a private dataset containing 3842 2-dimensional SD-OCT retina images, manually labeled by experts of the Franziskus Eye-Center, Muenster. While one of the competitors presents a better performance in terms of Dice score, the proposed method is significantly less computationally expensive. Thus, future research will focus on the proposed network's architecture to increase its segmentation performance while maintaining its computational efficiency.
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