Evidence map›Paper›PMID 27981396›Full record

ArticleMagma (New York, N.Y.)2017

Semi-automated myocardial segmentation of bright blood multi-gradient echo images improves reproducibility of myocardial contours and T2* determination.

Pandji Triadyaksa, Niek H J Prakken, Jelle Overbosch, Robin B Peters, J Martijn van Swieten, Matthijs Oudkerk, Paul E Sijens

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PubMed Full text at PMC Publisher
In one paragraph

Article in Magma (New York, N.Y.), 2017. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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.

  1. Article
  2. Cardiac TJournal of magnetic resonance imaging : JMRI · 2020
    Review
4 · The record

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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.

Pandji TriadyaksaCenter for Medical Imaging-North East Netherlands, University of Groningen, University Medical Center Groningen, EB45, 30001, 9700 RB, Groningen, The Netherlands. p.triadyaksa@umcg.nl.ORCID http://orcid.org/0000-0001-7536-3592
Niek H J PrakkenCenter for Medical Imaging-North East Netherlands, University of Groningen, University Medical Center Groningen, EB45, 30001, 9700 RB, Groningen, The Netherlands.
Jelle OverboschDepartment of Radiology, University of Groningen, University Medical Center Groningen, EB45, 30001, 9700 RB, Groningen, The Netherlands.
Robin B PetersDepartment of Radiology, University of Groningen, University Medical Center Groningen, EB45, 30001, 9700 RB, Groningen, The Netherlands.
J Martijn van SwietenDepartment of Radiology, University of Groningen, University Medical Center Groningen, EB45, 30001, 9700 RB, Groningen, The Netherlands.
Matthijs OudkerkCenter for Medical Imaging-North East Netherlands, University of Groningen, University Medical Center Groningen, EB45, 30001, 9700 RB, Groningen, The Netherlands.
Paul E SijensCenter for Medical Imaging-North East Netherlands, University of Groningen, University Medical Center Groningen, EB45, 30001, 9700 RB, Groningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesEarly detection of iron loading is affected by the reproducibility of myocardial contour assessment. A novel semi-automatic myocardial segmentation method is presented on contrast-optimized composite images and compared to the results of manual drawing. MATERIALS AND

methodsFifty-one short-axis slices at basal, mid-ventricular and apical locations from 17 patients were acquired by bright blood multi-gradient echo MRI. Four observers produced semi-automatic and manual myocardial contours on contrast-optimized composite images. The semi-automatic segmentation method relies on vector field convolution active contours to generate the endocardial contour. After creating radial pixel clusters on the myocardial wall, a combination of pixel-wise coefficient of variance (CoV) assessment and k-means clustering establishes the epicardial contour for each segment.

resultsCompared to manual drawing, semi-automatic myocardial segmentation lowers the variability of T2* quantification within and between observers (CoV of 12.05 vs. 13.86% and 14.43 vs. 16.01%) by improving contour reproducibility (P < 0.001). In the presence of iron loading, semi-automatic segmentation also lowers the T2* variability within and between observers (CoV of 13.14 vs. 15.19% and 15.91 vs. 17.28%).

conclusionApplication of semi-automatic myocardial segmentation on contrast-optimized composite images improves the reproducibility of T2* quantification.

Indexed as

Subtraction TechniqueAlgorithmsCardiomyopathiesHumansImage EnhancementImage Interpretation, Computer-AssistedIron OverloadMagnetic Resonance AngiographyMagnetic Resonance Imaging, CinePattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityBright blood myocardial T2*Iron loadingk-Means clusteringMagnetic resonance imagingVector field convolution active contour

Identifiers

PMID27981396
PMCPMC5440499

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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.