ArticleTranslational vision science & technology2020
Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 1 citations in OpenAlex.
- Endocrine modulation of cortical and retinal baseline perfusion across the menstrual cycle.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Article
- nnLoGoNet: a hybrid local-global network for retinal vessel segmentation with Skeleton Recall Loss.Scientific reports · 2026Article
- Impact of image preprocessing on dermatological OCTA vessel segmentation: a DERMA-OCTA study.Journal of biomedical optics · 2025Article
- DERMA-OCTA: A Comprehensive Dataset and Preprocessing Pipeline for Dermatological OCTA Vessel Segmentation.Scientific data · 2025Article
- Barriers in Healthcare to the Use of Optical Coherence Tomography Angiography in Multiple Sclerosis.Neurology and therapy · 2025Review
- Retinal capillary density among healthy Egyptian and South Asian students: an optical coherence tomography angiography study.International journal of ophthalmology · 2025Article
- VDMNet: A Deep Learning Framework with Vessel Dynamic Convolution and Multi-Scale Fusion for Retinal Vessel Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE).Bioengineering (Basel, Switzerland) · 2024Article
- Retinal small vessel pathology is associated with disease burden in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2024Article
- Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy.Eye (London, England) · 2024Article
- Segmentation of Low-Light Optical Coherence Tomography Angiography Images under the Constraints of Vascular Network Topology.Sensors (Basel, Switzerland) · 2024Article
- VMseg: Using spatial variance to automatically segment retinal non-perfusion on OCT-angiography.PloS one · 2024Article
- Fast optical coherence tomography angiography image acquisition and reconstruction pipeline for skin application.Biomedical optics express · 2023Article
- Comparison of Automated Thresholding Algorithms in Optical Coherence Tomography Angiography Image Analysis.Journal of clinical medicine · 2023Article
- Towards standardizing retinal optical coherence tomography angiography: a review.Light, science & applications · 2022Review
- Role of Anterior Segment-Optical Coherence Tomography Angiography in Acute Ocular Burns.Diagnostics (Basel, Switzerland) · 2022Review
- Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review.Journal of asthma and allergy · 2022Review
- Segmentation Error Correction of the Optical Coherence Tomography Angiography Images in Peer-Reviewed Studies.Journal of current ophthalmologyReview
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To generate the first open dataset of retinal parafoveal optical coherence tomography angiography (OCTA) images with associated ground truth manual segmentations, and to establish a standard for OCTA image segmentation by surveying a broad range of state-of-the-art vessel enhancement and binarization procedures. Methods: Handcrafted filters and neural network architectures were used to perform vessel enhancement. Thresholding methods and machine learning approaches were applied to obtain the final binarization. Evaluation was performed by using pixelwise metrics and newly proposed topological metrics. Finally, we compare the error in the computation of clinically relevant vascular network metrics (e.g., foveal avascular zone area and vessel density) across segmentation methods. Results: Our results show that, for the set of images considered, deep learning architectures (U-Net and CS-Net) achieve the best performance (Dice = 0.89). For applications where manually segmented data are not available to retrain these approaches, our findings suggest that optimally oriented flux (OOF) is the best handcrafted filter (Dice = 0.86). Moreover, our results show up to 25% differences in vessel density accuracy depending on the segmentation method used. Conclusions: In this study, we derive and validate the first open dataset of retinal parafoveal OCTA images with associated ground truth manual segmentations. Our findings should be taken into account when comparing the results of clinical studies and performing meta-analyses. Finally, we release our data and source code to support standardization efforts in OCTA image segmentation. Translational Relevance: This work establishes a standard for OCTA retinal image segmentation and introduces the importance of evaluating segmentation performance in terms of clinically relevant metrics.
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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 Socratic graph.