ArticleBiomedical optics express2015
Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema.
Article in Biomedical optics express, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 1 of them a synthesis that pooled it.
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Who cites it
117 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
- Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.Ophthalmology · 2021Trial
- Retinal cavitations in macular telangiectasia type 2 (MacTel): longitudinal structure-function correlations.The British journal of ophthalmology · 2021Trial
- Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.Ophthalmology · 2020Trial
- Dual-decoder multi-task network with graph attention mechanism for OCT retinal layer and fluid segmentation.BMC ophthalmology · 2026Article
- Retinal layer segmentation in OCT images with a 2.5D cross-slice feature fusion module for glaucoma assessment.Biomedical optics express · 2026Article
- Language-assisted multimodal convolutional transformer pipeline for retinal lesions segmentation.Scientific reports · 2026Article
- Query-Driven Retinal Layer Segmentation in OCT Using Cross-Attentive Feature Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Language assisted learnable hyperdimensional computing framework for retinal disease classification.Scientific reports · 2026Article
- Evaluation of classification performance for six types of fundus diseases in OCT images based on multi-source training strategy.Frontiers in medicine · 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
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- 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
- Self-knowledge distillation-empowered directional connectivity transformer for microbial keratitis biomarkers segmentation on slit-lamp photography.Medical image analysis · 2025Article
- OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers.Scientific data · 2025Article
- From Image to Sequence: Exploring Vision Transformers for Optical Coherence Tomography Classification.Journal of medical signals and sensors · 2025Article
- Revolutionizing healthcare: a comparative insight into deep learning's role in medical imaging.Scientific reports · 2024Article
- BreakNet: discontinuity-resilient multi-scale transformer segmentation of retinal layers.Biomedical optics express · 2024Article
- Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.Journal of imaging informatics in medicine · 2024Article
57 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.
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