ArticleBiomedical optics express2018
DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- Outer retinal band segmentation in healthy subjects: comparative study between human grading and deep convolutional neural networks.Quantitative imaging in medicine and surgery · 2026Article
- The Application of a Deep Learning Algorithm for the Segmentation of Retinal Nerve Fiber Layer Across Different Optic Neuropathies.Translational vision science & technology · 2026Article
- Multi-scale adaptive fusion network for retinal layer and fluid segmentation in optical coherence tomography B-scans.Scientific reports · 2026Article
- Deep Learning Analysis of Widefield Cornea Endothelial Imaging in Fuchs Dystrophy.Ophthalmology science · 2026Article
- 3D Structural Phenotype of the Optic Nerve Head in Glaucoma and Myopia-A Key to Improving Glaucoma Diagnosis in Myopic Populations.American journal of ophthalmology · 2026Article
- Deep learning method for semi-automated segmentation of optic nerve head tissues in optical coherence tomography images.Experimental eye research · 2025Article
- MDFormer: a multi-scale dense dilated transformer model for 3D medical image segmentation.Scientific reports · 2025Article
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2025Article
- Diagnosis of early glaucoma likely combined with high myopia by integrating OCT thickness map and standard automated and Pulsar perimetries.Scientific reports · 2025Article
- DELR-Net: a network for 3D multimodal medical image registration in more lightweight application scenarios.Abdominal radiology (New York) · 2025Article
- Detection of Brain Tumor Employing Residual Network-based Optimized Deep Learning.Current computer-aided drug design · 2025Article
- Choroidalyzer: An Open-Source, End-to-End Pipeline for Choroidal Analysis in Optical Coherence Tomography.Investigative ophthalmology & visual science · 2024Article
- Are Macula or Optic Nerve Head Structures Better at Diagnosing Glaucoma? An Answer Using Artificial Intelligence and Wide-Field Optical Coherence Tomography.Translational vision science & technology · 2024Article
- Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multiscale Encoders and Decoders.Bioengineering (Basel, Switzerland) · 2023Article
- How Myopia and Glaucoma Influence the Biomechanical Susceptibility of the Optic Nerve Head.Investigative ophthalmology & visual science · 2023Article
- Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism.Journal of autism and developmental disorders · 2023Article
- Deep learning segmentation of the tear fluid reservoir under the sclera lens in optical coherence tomography images.Biomedical optics express · 2023Article
- Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.Medical image analysis · 2023Review
16 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm (custom U-NET) was designed and trained to segment 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall Dice coefficient (mean of all tissues) was 0.91 ± 0.05 when assessed against manual segmentations performed by an expert observer. Further, we automatically extracted six clinically relevant neural and connective tissue structural parameters from the segmented tissues. We offer here a robust segmentation framework that could also be extended to the 3D segmentation of the ONH tissues.
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