ArticleBiomedical optics express2023
Annotation-efficient learning for OCT segmentation.
Article in Biomedical optics express, 2023. 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.
- Structural-prior guided and feature-enhanced transformer with masked image modeling pretraining for retinal layers and fluid segmentation in macular edema OCT images.Biomedical optics express · 2025Article
- Spatial and Temporal Changes in Choroid Morphology Associated With Long-Duration Spaceflight.Investigative ophthalmology & visual science · 2025Article
- Semi-supervised 3D retinal fluid segmentation via correlation mutual learning with global reasoning attention.Biomedical optics express · 2024Article
- Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging.Biomedical optics express · 2024Article
- 30 Years of Optical Coherence Tomography: introduction to the feature issue.Biomedical optics express · 2023Article
- Annotation-efficient learning for OCT segmentation.Biomedical optics express · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
Deep learning has been successfully applied to OCT segmentation. However, for data from different manufacturers and imaging protocols, and for different regions of interest (ROIs), it requires laborious and time-consuming data annotation and training, which is undesirable in many scenarios, such as surgical navigation and multi-center clinical trials. Here we propose an annotation-efficient learning method for OCT segmentation that could significantly reduce annotation costs. Leveraging self-supervised generative learning, we train a Transformer-based model to learn the OCT imagery. Then we connect the trained Transformer-based encoder to a CNN-based decoder, to learn the dense pixel-wise prediction in OCT segmentation. These training phases use open-access data and thus incur no annotation costs, and the pre-trained model can be adapted to different data and ROIs without re-training. Based on the greedy approximation for the k-center problem, we also introduce an algorithm for the selective annotation of the target data. We verified our method on publicly-available and private OCT datasets. Compared to the widely-used U-Net model with 100% training data, our method only requires
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