ArticleScientific data2023
OIMHS: An Optical Coherence Tomography Image Dataset Based on Macular Hole Manual Segmentation.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.ACM transactions on computing for healthcare · 2026Article
- A three-dimensional multi-modal foundation model for optical coherence tomography.Nature biomedical engineering · 2026Article
- Quantifying Explainability in OCT Segmentation of Macular Holes and Cysts: A SHAP-Based Coverage and Factor Contribution Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models.Findings of ACL. NAACL · 2025Article
- Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants.Journal of imaging · 2025Article
- Segment Anything in Optical Coherence Tomography: SAM 2 for Volumetric Segmentation of Retinal Biomarkers.Bioengineering (Basel, Switzerland) · 2024Article
- Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview.Diagnostics (Basel, Switzerland) · 2024Review
- OIMHS: An Optical Coherence Tomography Image Dataset Based on Macular Hole Manual Segmentation.Scientific data · 2023Article
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Authors and funding
10 authors.
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Abstract
Macular holes, one of the most common macular diseases, require timely treatment. The morphological changes on optical coherence tomography (OCT) images provided an opportunity for direct observation of the disease, and accurate segmentation was needed to identify and quantify the lesions. Developments of such algorithms had been obstructed by a lack of high-quality datasets (the OCT images and the corresponding gold standard macular hole segmentation labels), especially for supervised learning-based segmentation algorithms. In such context, we established a large OCT image macular hole segmentation (OIMHS) dataset with 3859 B-scan images of 119 patients, and each image provided four segmentation labels: retina, macular hole, intraretinal cysts, and choroid. This dataset offered an excellent opportunity for investigating the accuracy and reliability of different segmentation algorithms for macular holes and a new research insight into the further development of clinical research for macular diseases, which included the retina, lesions, and choroid in quantitative analyses.
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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.