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dc.contributor.authorPETERSEN, SE
dc.contributor.authorRobinson, R
dc.contributor.authorValindria, V
dc.contributor.authorBai, W
dc.contributor.authorOktay, O
dc.contributor.authorKainz, B
dc.contributor.authorSuzuki, H
dc.contributor.authorSanghvi, M
dc.contributor.authorAung, N
dc.contributor.authorPaiva, J
dc.contributor.authorZemrak, F
dc.contributor.authorFung, K
dc.contributor.authorLukaschuk, E
dc.contributor.authorLee, A M
dc.contributor.authorCarapella, V
dc.contributor.authorKim, Y J
dc.contributor.authorPiechnik, S
dc.contributor.authorNeubauer, S
dc.contributor.authorPage, C
dc.contributor.authorMatthews, P
dc.contributor.authorRueckert, D
dc.contributor.authorGlocker, B
dc.date.accessioned2019-04-05T16:47:49Z
dc.date.available2019-02-03
dc.date.available2019-04-05T16:47:49Z
dc.date.issued2019-03-14
dc.identifier.issn1097-6647
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/56743
dc.description.abstractBackground: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC. Results: We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions: We show that Reverse classification accuracy has the potential for accurate and fully automatic segmenten_US
dc.description.sponsorshipMRC eMedLab Medical Bioinformatics infrastructure, supported by the Medical Research Council (grant number MR/L016311/1).en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.relation.ispartofJournal of Cardiovascular Magnetic Resonance
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectAutomatic quality controlen_US
dc.subjectPopulation imagingen_US
dc.subjectSegmentationen_US
dc.titleAutomated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging studyen_US
dc.title.alternativeAutomated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Studyen_US
dc.typeArticleen_US
dc.rights.holder© The Author(s). 2019
dc.identifier.doihttps://doi.org/10.1186/s12968-019-0523-x
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2019-02-03
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funder“Creation of cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource”::British Heart Foundationen_US
qmul.funder“Creation of cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource”::British Heart Foundationen_US
qmul.funder“Creation of cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource”::British Heart Foundationen_US
qmul.funder“Creation of cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource”::British Heart Foundationen_US
qmul.funder“Creation of cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource”::British Heart Foundationen_US
qmul.funder“Creation of cardiovascular magnetic resonance imaging reference standard for the UK Biobank imaging resource”::British Heart Foundationen_US


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Attribution 3.0 United States
Except where otherwise noted, this item's license is described as Attribution 3.0 United States