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Segmentation Uncertainty Quantification in Cardiac Propagation Models

hal.structure.identifierScientific Computing and Imaging Institute [SCI Institute]
dc.contributor.authorTATE, Jess
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
dc.contributor.authorZEMZEMI, Nejib
hal.structure.identifierScientific Computing and Imaging Institute [SCI Institute]
dc.contributor.authorELHABIAN, Shireen
hal.structure.identifierSlovak University of Technology in Bratislava [STU]
dc.contributor.authorONDRUSOVÁ, Beáta
hal.structure.identifierUniversity Medical Center [Utrecht] [UMCU]
dc.contributor.authorBOONSTRA, Machteld
hal.structure.identifierUniversity Medical Center [Utrecht] [UMCU]
dc.contributor.authorVAN DAM, Peter
hal.structure.identifierScientific Computing and Imaging Institute [SCI Institute]
dc.contributor.authorNARAYAN, Akil
hal.structure.identifierNortheastern University [Boston]
dc.contributor.authorBROOKS, Dana
hal.structure.identifierScientific Computing and Imaging Institute [SCI Institute]
dc.contributor.authorMACLEOD, Rob
dc.date.accessioned2024-04-04T02:36:24Z
dc.date.available2024-04-04T02:36:24Z
dc.date.issued2022-09-07
dc.date.conference2022-09-04
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190715
dc.description.abstractEnA key part of patient-specific cardiac simulations is segmentation, yet the impact of this subjective and errorprone process hasn't been quantified in most simulation pipelines. In this study we quantify the dependence of a cardiac propagation model on from segmentation variability. We used statistical shape modeling and polynomial Chaos (PC) to capture segmentation variability dependence and applied its affects to a propagation model. We evaluated the predicted local activation times (LATs) an body surface potentials (BSPs) from two modeling pipelines: an EIkonal propagation model and a surfacebased fastest route model. The predicted uncertainty due to segmentation shape variability was distributed near the base of the heart and near high amplitude torso potential regions. Our results suggest that modeling pipelines may have to accommodate segmentation errors if regions of interest correspond to high segmentation error. Further, even small errors could proliferate if modeling results are used to to feed further computations, such as ECGI.
dc.language.isoen
dc.title.enSegmentation Uncertainty Quantification in Cardiac Propagation Models
dc.typeCommunication dans un congrès
dc.subject.halSciences de l'ingénieur [physics]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleCinC 2022 - 49th Computing in Cardiology Conference
bordeaux.countryFI
bordeaux.conference.cityTampere
bordeaux.peerReviewedoui
hal.identifierhal-03933769
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2022-09-07
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03933769v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2022-09-07&rft.au=TATE,%20Jess&ZEMZEMI,%20Nejib&ELHABIAN,%20Shireen&ONDRUSOV%C3%81,%20Be%C3%A1ta&BOONSTRA,%20Machteld&rft.genre=unknown


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