ArticleMedical image analysis2016
Quantitative analysis of retinal OCT.
Article in Medical image analysis, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 58 citations in OpenAlex.
- Octascope: A Lightweight Pre-Trained Model for Optical Coherence Tomography.IEEE access : practical innovations, open solutions · 2025Article
- Automated quantification of photoreceptor outer segments in developing and degenerating retinas on microscopy images across scales.Frontiers in molecular neuroscience · 2024Article
- A State-Of-The-Art Review on Coronary Artery Border Segmentation Algorithms for Intravascular Ultrasound (IVUS) Images.Cardiovascular engineering and technology · 2023Review
- CTS-Net: A Segmentation Network for Glaucoma Optical Coherence Tomography Retinal Layer Images.Bioengineering (Basel, Switzerland) · 2023Article
- 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
- Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Automated segmentation of choroidal layers from 3-dimensional macular optical coherence tomography scans.Journal of neuroscience methods · 2021Article
- Livelayer: a semi-automatic software program for segmentation of layers and diabetic macular edema in optical coherence tomography images.Scientific reports · 2021Article
- Roughness of retinal layers in Alzheimer's disease.Scientific reports · 2021Article
- Genetic variation affects morphological retinal phenotypes extracted from UK Biobank optical coherence tomography images.PLoS genetics · 2021Article
- Correlation between Macular Pigment Optical Density and Neural Thickness and Volume of the Retina.Nutrients · 2020Article
- Focal alteration of the intraretinal layers in neurodegenerative disorders.Annals of eye science · 2020Article
- Observational
- Spatial analysis of thickness changes in ten retinal layers of Alzheimer's disease patients based on optical coherence tomography.Scientific reports · 2019Article
- Retinal vessel optical coherence tomography images for anemia screening.Medical & biological engineering & computing · 2019Article
- The correlation between optical coherence tomography retinal shape irregularity and axial length.PloS one · 2019Observational
- Quantitative 3D Analysis of Coronary Wall Morphology in Heart Transplant Patients: OCT-Assessed Cardiac Allograft Vasculopathy Progression.Medical image analysis · 2018Article
- Long-term Characterization of Retinal Degeneration in Royal College of Surgeons Rats Using Spectral-Domain Optical Coherence Tomography.Investigative ophthalmology & visual science · 2017Article
- The Edge Detectors Suitable for Retinal OCT Image Segmentation.Journal of healthcare engineering · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Clinical acceptance of 3-D OCT retinal imaging brought rapid development of quantitative 3-D analysis of retinal layers, vasculature, retinal lesions as well as facilitated new research in retinal diseases. One of the cornerstones of many such analyses is segmentation and thickness quantification of retinal layers and the choroid, with an inherently 3-D simultaneous multi-layer LOGISMOS (Layered Optimal Graph Image Segmentation for Multiple Objects and Surfaces) segmentation approach being extremely well suited for the task. Once retinal layers are segmented, regional thickness, brightness, or texture-based indices of individual layers can be easily determined and thus contribute to our understanding of retinal or optic nerve head (ONH) disease processes and can be employed for determination of disease status, treatment responses, visual function, etc. Out of many applications, examples provided in this paper focus on image-guided therapy and outcome prediction in age-related macular degeneration and on assessing visual function from retinal layer structure in glaucoma.
Indexed as
Identifiers
What OpenQuestion holds
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.