ArticleMedical image analysis2021
Structured layer surface segmentation for retina OCT using fully convolutional regression networks.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review of optimization strategies for deep and machine learning in diabetic macular edema.Frontiers in artificial intelligence · 2026Pooled it
- VALIDATION OF A DEEP LEARNING-BASED ALGORITHM FOR SEGMENTATION OF THE ELLIPSOID ZONE ON OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AN USH2A-RELATED RETINAL DEGENERATION CLINICAL TRIAL.Retina (Philadelphia, Pa.) · 2022Trial
- Retinal layer segmentation in OCT images with a 2.5D cross-slice feature fusion module for glaucoma assessment.Biomedical optics express · 2026Article
- Unsupervised OCT Image Interpolation Using Deformable Registration and generative models.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Optical Coherence Tomography Harmonization via Dual Diffusion Implicit Bridges.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis.NPJ digital medicine · 2025Article
- ROQUS: a retinal OCT quality and usability score.Biomedical optics express · 2025Article
- Visible light optical coherence tomography for assessing retinal structure and function in diseased mouse models.Biomedical optics express · 2025Article
- Anomaly Detection in Retinal OCT Images With Deep Learning-Based Knowledge Distillation.Translational vision science & technology · 2025Article
- Looking outside the box with a pathology aware AI approach for analyzing OCT retinal images in Stargardt disease.Scientific reports · 2025Article
- RetOCTNet: Deep Learning-Based Segmentation of OCT Images Following Retinal Ganglion Cell Injury.Translational vision science & technology · 2025Article
- Optical Coherence Tomography Harmonization with Anatomy-Guided Latent Metric Schrödinger Bridges.Advances in neural information processing systems · 2025Article
- Article
- Performance of automated machine learning in detecting fundus diseases based on ophthalmologic B-scan ultrasound images.BMJ open ophthalmology · 2024Article
- Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.Journal of imaging informatics in medicine · 2024Article
- AI-based 3D analysis of retinal vasculature associated with retinal diseases using OCT angiography.Biomedical optics express · 2024Article
- Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview.Diagnostics (Basel, Switzerland) · 2024Review
- Deep Neural Networks for Automated Outer Plexiform Layer Subsidence Detection on Retinal OCT of Patients With Intermediate AMD.Translational vision science & technology · 2024Article
- Hybrid deep learning and optimal graph search method for optical coherence tomography layer segmentation in diseases affecting the optic nerve.Biomedical optics express · 2024Article
- Self-attention CNN for retinal layer segmentation in OCT.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer resolution which has been widely used for scanning the retina. Retinal layers are important biomarkers for many diseases. Accurate automated algorithms for segmenting smooth continuous layer surfaces with correct hierarchy (topology) are important for automated retinal thickness and surface shape analysis. State-of-the-art methods typically use a two step process. Firstly, a trained classifier is used to label each pixel into either background and layers or boundaries and non-boundaries. Secondly, the desired smooth surfaces with the correct topology are extracted by graph methods (e.g., graph cut). Data driven methods like deep networks have shown great ability for the pixel classification step, but to date have not been able to extract structured smooth continuous surfaces with topological constraints in the second step. In this paper, we combine these two steps into a unified deep learning framework by directly modeling the distribution of the surface positions. Smooth, continuous, and topologically correct surfaces are obtained in a single feed forward operation. The proposed method was evaluated on two publicly available data sets of healthy controls and subjects with either multiple sclerosis or diabetic macular edema, and is shown to achieve state-of-the art performance with sub-pixel accuracy.
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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.