Evidence map›Paper›PMID 33344049›Full record

ArticleTranslational vision science & technology2020

Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics.

Ylenia Giarratano, Eleonora Bianchi, Calum Gray, Andrew Morris, Tom MacGillivray, Baljean Dhillon, Miguel O Bernabeu

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact, top 92% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 citations in OpenAlex.

  1. 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 · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Retinal small vessel pathology is associated with disease burden in multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Review
  18. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Ylenia GiarratanoUsher Institute, University of Edinburgh, Edinburgh, UK.
Eleonora BianchiPrincess Alexandra Eye Pavilion, NHS Lothian, Edinburgh, UK.
Calum GrayEdinburgh Imaging, University of Edinburgh, Edinburgh, UK.
Andrew MorrisUsher Institute, University of Edinburgh, Edinburgh, UK.
Tom MacGillivrayCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Baljean DhillonPrincess Alexandra Eye Pavilion, NHS Lothian, Edinburgh, UK.
Miguel O BernabeuUsher Institute, University of Edinburgh, Edinburgh, UK.
University of Edinburgh · GBNHS Lothian · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

BenchmarkingTomography, Optical CoherenceFluorescein AngiographyNeural Networks, ComputerRetinaautomated segmentationoptical coherence tomography angiographyretinal vasculature

Identifiers

PMID33344049
PMCPMC7718823
OpenAlexW3029287515

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.