ArticleIEEE transactions on instrumentation and measurement2024
Lightweight Retinal Layer Segmentation With Global Reasoning.
Article in IEEE transactions on instrumentation and measurement, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Demystifying Artificial Intelligence: A Systematic Review of Explainable Artificial Intelligence in Medical Imaging.Sensors (Basel, Switzerland) · 2026Pooled it
- Dual-decoder multi-task network with graph attention mechanism for OCT retinal layer and fluid segmentation.BMC ophthalmology · 2026Article
- Query-Driven Retinal Layer Segmentation in OCT Using Cross-Attentive Feature Learning.Diagnostics (Basel, Switzerland) · 2026Article
- AMD-UPerNet: a tool for retinal layer and fluid assessment in age-related macular degeneration.BMC medical imaging · 2026Article
- Multi-scale adaptive fusion network for retinal layer and fluid segmentation in optical coherence tomography B-scans.Scientific reports · 2026Article
- 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
- Power system security and protection considering the integration of new energy power plants.Scientific reports · 2025Article
- Three-Dimensional Quantification of Macular OCT Alterations Improves the Diagnostic Performance of Artificial Intelligence Models.Translational vision science & technology · 2025Article
- Quantifying anterior segment vascular changes in thyroid eye disease using optical coherence tomography angiography.BMC medical imaging · 2025Article
- Fusing multispectral information for retinal layer segmentation.NPJ digital medicine · 2025Article
- A Review of Application of Deep Learning in Endoscopic Image Processing.Journal of imaging · 2024Review
- HDB-Net: hierarchical dual-branch network for retinal layer segmentation in diseased OCT images.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Automatic retinal layer segmentation with medical images, such as optical coherence tomography (OCT) images, serves as an important tool for diagnosing ophthalmic diseases. However, it is challenging to achieve accurate segmentation due to low contrast and blood flow noises presented in the images. In addition, the algorithm should be light-weight to be deployed for practical clinical applications. Therefore, it is desired to design a light-weight network with high performance for retinal layer segmentation. In this paper, we propose LightReSeg for retinal layer segmentation which can be applied to OCT images. Specifically, our approach follows an encoder-decoder structure, where the encoder part employs multi-scale feature extraction and a Transformer block for fully exploiting the semantic information of feature maps at all scales and making the features have better global reasoning capabilities, while the decoder part, we design a multi-scale asymmetric attention (MAA) module for preserving the semantic information at each encoder scale. The experiments show that our approach achieves a better segmentation performance compared to the current state-of-the-art method TransUnet with 105.7M parameters on both our collected dataset and two other public datasets, with only 3.3M parameters.
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Registered trials
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