Evidence map›Paper›PMID 36396540›Full record

ArticleJournal of optometry2022

MSGANet-RAV: A multiscale guided attention network for artery-vein segmentation and classification from optic disc and retinal images.

A Z M Ehtesham Chowdhury, Graham Mann, William Huxley Morgan, Aleksandar Vukmirovic, Andrew Mehnert, Ferdous Sohel

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of optometry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.4field-weighted citation impact, top 11% 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

2 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
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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

6 authors at 2 institutions in 1 country.

A Z M Ehtesham ChowdhurySchool of Information Technology, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia.
Graham MannSchool of Information Technology, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia.
William Huxley MorganLions Eye Institute, 2 Verdun Street, Nedlands, WA 6009, Australia; Centre for Ophthalmology and Visual Science, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia.
Aleksandar VukmirovicLions Eye Institute, 2 Verdun Street, Nedlands, WA 6009, Australia; Centre for Ophthalmology and Visual Science, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia.
Andrew MehnertLions Eye Institute, 2 Verdun Street, Nedlands, WA 6009, Australia; Centre for Ophthalmology and Visual Science, The University of Western Australia, 35 Stirling Highway, Perth, WA 6009, Australia.
Ferdous SohelSchool of Information Technology, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia. Electronic address: f.sohel@murdoch.edu.au.
Lions Eye Institute · AUMurdoch University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRetinal and optic disc images are used to assess changes in the retinal vasculature. These can be changes associated with diseases such as diabetic retinopathy and glaucoma or induced using ophthalmodynamometry to measure arterial and venous pressure. Key steps toward automating the assessment of these changes are the segmentation and classification of the veins and arteries. However, such segmentation and classification are still required to be manually labelled by experts. Such automated labelling is challenging because of the complex morphology, anatomical variations, alterations due to disease and scarcity of labelled data for algorithm development. We present a deep machine learning solution called the multiscale guided attention network for retinal artery and vein segmentation and classification (MSGANet-RAV).

methodsMSGANet-RAV was developed and tested on 383 colour clinical optic disc images from LEI-CENTRAL, constructed in-house and 40 colour fundus images from the AV-DRIVE public dataset. The datasets have a mean optic disc occupancy per image of 60.6% and 2.18%, respectively. MSGANet-RAV is a U-shaped encoder-decoder network, where the encoder extracts multiscale features, and the decoder includes a sequence of self-attention modules. The self-attention modules explore, guide and incorporate vessel-specific structural and contextual feature information to segment and classify central optic disc and retinal vessel pixels.

resultsMSGANet-RAV achieved a pixel classification accuracy of 93.15%, sensitivity of 92.19%, and specificity of 94.13% on LEI-CENTRAL, outperforming several reference models. It similarly performed highly on AV-DRIVE with an accuracy, sensitivity and specificity of 95.48%, 93.59% and 97.27%, respectively.

conclusionThe results show the efficacy of MSGANet-RAV for identifying central optic disc and retinal arteries and veins. The method can be used in automated systems designed to assess vascular changes in retinal and optic disc images quantitatively.

Indexed as

GlaucomaOptic DiskArteriesHumansRetinaRetinal VesselsArtery-vein classificationDeep learningFundus imageGuided attentionOptic disc image

Identifiers

PMID36396540
PMCPMC9732479
OpenAlexW4309019371

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.