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Uncertainty Quantification of the Effects of Segmentation Variability in ECGI

hal.structure.identifierUniversity of Utah
dc.contributor.authorTATE, Jess
hal.structure.identifierUniversity of Utah
dc.contributor.authorGOOD, Wilson
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
dc.contributor.authorZEMZEMI, Nejib
hal.structure.identifierUtrecht Brain Center [UMC]
dc.contributor.authorBOONSTRA, Machteld
hal.structure.identifierUtrecht Brain Center [UMC]
dc.contributor.authorVAN DAM, Peter
hal.structure.identifierNortheastern University [Boston]
dc.contributor.authorBROOKS, Dana
hal.structure.identifierUniversity of Utah
dc.contributor.authorNARAYAN, Akil
hal.structure.identifierUniversity of Utah
dc.contributor.authorMACLEOD, Rob
dc.contributor.editorDaniel B. Ennis
dc.contributor.editorLuigi E. Perotti
dc.contributor.editorVicky Y. Wang
dc.date.accessioned2024-04-04T02:40:15Z
dc.date.available2024-04-04T02:40:15Z
dc.date.issued2021-06-18
dc.date.conference2021-06-21
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191049
dc.description.abstractEnDespite advances in many of the techniques used in Electrocardiographic Imaging (ECGI), uncertainty remains insufficiently quantified for many aspects of the pipeline. The effect of geometric uncertainty, particularly due to segmentation variability, may be the least explored to date. We use statistical shape modeling and uncertainty quantification (UQ) to compute the effect of segmentation variability on ECGI solutions. The shape model was made with Shapeworks from nine segmentations of the same patient and incorporated into an ECGI pipeline. We computed uncertainty of the pericardial potentials and local activation times (LATs) using polynomial chaos expansion (PCE) implemented in UncertainSCI. Uncertainty in pericardial potentials from segmentation variation mirrored areas of high variability in the shape model, near the base of the heart and the right ventricular outflow tract, and that ECGI was less sensitive to uncertainty in the posterior region of the heart. Subsequently LAT calculations could vary dramatically due to segmentation variability, with a standard deviation as high as 126ms, yet mainly in regions with low conduction velocity. Our shape modeling and UQ pipeline presented possible uncertainty in ECGI due to segmentation variability and can be used by researchers to reduce said uncertainty or mitigate its effects. The demonstrated use of statistical shape modeling and UQ can also be extended to other types of modeling pipelines.
dc.language.isoen
dc.publisherSpringer International Publishing
dc.source.titleLecture Notes in Computer Science
dc.subject.enElectrocardiographic Imaging
dc.subject.enShape Analysis
dc.subject.enUncertainty Quantification
dc.title.enUncertainty Quantification of the Effects of Segmentation Variability in ECGI
dc.typeCommunication dans un congrès
dc.identifier.doi10.1007/978-3-030-78710-3_49
dc.subject.halInformatique [cs]/Modélisation et simulation
bordeaux.page515-522
bordeaux.volumeLNCS-12738
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleFIMH 2021 - 11th International Conference on Functional Imaging and Modeling of the Heart
bordeaux.countryUS
bordeaux.title.proceedingLecture Notes in Computer Science
bordeaux.conference.cityStanford
bordeaux.peerReviewedoui
hal.identifierhal-03805280
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2021-06-25
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03805280v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Lecture%20Notes%20in%20Computer%20Science&rft.date=2021-06-18&rft.volume=LNCS-12738&rft.spage=515-522&rft.epage=515-522&rft.au=TATE,%20Jess&GOOD,%20Wilson&ZEMZEMI,%20Nejib&BOONSTRA,%20Machteld&VAN%20DAM,%20Peter&rft.genre=unknown


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