ArticleInternational journal of ophthalmology2019
Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images.
Article in International journal of ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- AI-aided segmentation of four types of drusen in volumetric OCT.Biomedical optics express · 2025Article
- Human expert grading versus automated quantification of fluid volumes in nAMD, DME and BRVO.Scientific reports · 2025Article
- Novel Method to Measure Volumes of Retinal Specific Entities.Journal of clinical medicine · 2024Article
- Deep Learning in Neovascular Age-Related Macular Degeneration.Medicina (Kaunas, Lithuania) · 2024Review
- Article
- Artificial intelligence applications in ophthalmic optical coherence tomography: a 12-year bibliometric analysis.International journal of ophthalmology · 2024Article
- Longitudinal deep network for consistent OCT layer segmentation.Biomedical optics express · 2023Article
- A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images.Scientific reports · 2023Article
- Automated artificial intelligence-based system for clinical follow-up of patients with age-related macular degeneration.Acta ophthalmologica · 2022Article
- Etiology of Macular Edema Defined by Deep Learning in Optical Coherence Tomography Scans.Translational vision science & technology · 2022Article
- Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration.Biomedical optics express · 2022Article
- Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2022Review
- OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN.Sensors (Basel, Switzerland) · 2022Article
- Development and Application of an Intelligent Diagnosis System for Retinal Vein Occlusion Based on Deep Learning.Disease markers · 2022Article
- Three-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning.International journal of ophthalmology · 2022Article
- Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning.Ophthalmology · 2021Article
- Towards label-free 3D segmentation of optical coherence tomography images of the optic nerve head using deep learning.Biomedical optics express · 2020Article
- Automated Segmentation of Retinal Fluid Volumes From Structural and Angiographic Optical Coherence Tomography Using Deep Learning.Translational vision science & technology · 2020Article
- Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review.Translational vision science & technology · 2020Review
- Quantifying Changes on OCT in Eyes Receiving Treatment for Neovascular Age-Related Macular Degeneration.Ophthalmology scienceArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.
methodsA two-dimensional (2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography (OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional (3D) fully convolutional network for segmentation in the retinal OCT images.
resultsThe algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F
conclusionThe OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.
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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.