ArticleBiomedical optics express2021
Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.
- 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
- Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation.Scientific reports · 2026Article
- Text-Driven Weakly Supervised OCT Lesion Segmentation With Structural Guidance.IEEE journal of biomedical and health informatics · 2026Article
- Multi-scale adaptive fusion network for retinal layer and fluid segmentation in optical coherence tomography B-scans.Scientific reports · 2026Article
- Dual coordinate attention (DCA) network for accurate cerebral vascular endothelium segmentation in OCT images.Scientific reports · 2026Article
- Language assisted learnable hyperdimensional computing framework for retinal disease classification.Scientific reports · 2026Article
- Deep learning method for semi-automated segmentation of optic nerve head tissues in optical coherence tomography images.Experimental eye research · 2025Article
- Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis.NPJ digital medicine · 2025Article
- Three-Dimensional Quantification of Macular OCT Alterations Improves the Diagnostic Performance of Artificial Intelligence Models.Translational vision science & technology · 2025Article
- OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers.Scientific data · 2025Article
- Fusing multispectral information for retinal layer segmentation.NPJ digital medicine · 2025Article
- Image-text guided fundus vessel segmentation via attention mechanism and gated residual learning.Frontiers in cell and developmental biology · 2025Article
- High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks.Brain informatics · 2024Article
- Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.Journal of imaging informatics in medicine · 2024Article
- Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- HDB-Net: hierarchical dual-branch network for retinal layer segmentation in diseased OCT images.Biomedical optics express · 2024Article
- Effects of Acute Mental Stress on Choroidal Thickness.Bioengineering (Basel, Switzerland) · 2024Article
- African swine fever virus proteins against host antiviral innate immunity and their implications for vaccine development.Open veterinary journal · 2024Review
- Self-attention CNN for retinal layer segmentation in OCT.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we develop a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conduct experiments on human peripapillary retinal OCT images. We also provide public access to the collected dataset, which might contribute to the research in the field of biomedical image processing. The Dice score of the proposed segmentation network is 0.820 ± 0.001 and the pixel accuracy is 0.830 ± 0.002, both of which outperform those from other state-of-the-art techniques.
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Registered trials
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.