ArticleFrontiers in cell and developmental biology2022
Choroidal layer segmentation in OCT images by a boundary enhancement network.
Article in Frontiers in cell and developmental biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Spatial and Temporal Changes in Choroid Morphology Associated With Long-Duration Spaceflight.Investigative ophthalmology & visual science · 2025Article
- A novel multi-scale and fine-grained network for large choroidal vessels segmentation in OCT.Frontiers in cell and developmental biology · 2025Article
- ChoroidSeg-ViT: A Transformer Model for Choroid Layer Segmentation Based on a Mixed Attention Feature Enhancement Mechanism.Translational vision science & technology · 2024Article
- Applications of Artificial Intelligence in Choroid Visualization for Myopia: A Comprehensive Scoping Review.Middle East African journal of ophthalmologyArticle
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
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Abstract
Morphological changes of the choroid have been proved to be associated with the occurrence and pathological mechanism of many ophthalmic diseases. Optical Coherence Tomography (OCT) is a non-invasive technique for imaging of ocular biological tissues, that can reveal the structure of the retinal and choroidal layers in micron-scale resolution. However, unlike the retinal layer, the interface between the choroidal layer and the sclera is ambiguous in OCT, which makes it difficult for ophthalmologists to identify with certainty. In this paper, we propose a novel boundary-enhanced encoder-decoder architecture for choroid segmentation in retinal OCT images, in which a Boundary Enhancement Module (BEM) forms the backbone of each encoder-decoder layer. The BEM consists of three parallel branches: 1) a Feature Extraction Branch (FEB) to obtain feature maps with different receptive fields; 2) a Channel Enhancement Branch (CEB) to extract the boundary information of different channels; and 3) a Boundary Activation Branch (BAB) to enhance the boundary information
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Registered trials
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