Handling confounding variables in statistical shape analysis - application to cardiac remodelling

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author Bernardino Perez, Gabriel
• dc.contributor.author Benkarim, Oualid M.
• dc.contributor.author Sanz de la Garza, Maria
• dc.contributor.author Prat Gonzàlez, Susanna
• dc.contributor.author Sepúlveda-Martínez, Álvaro
• dc.contributor.author Crispi Brillas, Fàtima
• dc.contributor.author Sitges, Marta
• dc.contributor.author Butakoff, Constantine
• dc.contributor.author Craene, Mathieu de
• dc.contributor.author Bijnens, Bart
• dc.contributor.author González Ballester, Miguel Ángel, 1973-
• dc.date.accessioned 2020-07-21T09:51:15Z
• dc.date.issued 2020
• dc.description.abstract Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods: confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets.en
• dc.description.sponsorship This study was partially supported by the Spanish Ministry of Economy and Competitiveness (grant DEP2013-44923- P, TIN2014-52923-R; Maria de Maeztu Units of Excellence Programme - MDM-2015-0502), el Fondo Europeo de Desarrollo Regional (FEDER) , the European Union under the Horizon 2020 Programme for Research, Innovation (grant agreement No. 642676 CardioFunXion) and Erasmus+ Programme (Framework Agreement number: 2013-0040), la Caixa Foundation (LCF/PR/GN14/10270005, LCF/PR/GN18/10310003), Instituto de Salud Carlos III (PI14/00226, PI17/00675) integrated in the Plan Nacional I+D+I and AGAUR 2017 SGR grant n 1531.
• dc.format.mimetype application/pdf
• dc.identifier.citation Bernardino G, Benkarim Q, Sanz de la Garza M, Prat-Gonzàlez S, Sepulveda-Martinez A, Crispi F, Sitges M, Butakoff C, De Craene M, Bijnens B, González Ballester MA. Handling confounding variables in statistical shape analysis - application to cardiac remodelling. Med Image Anal. 2020 Jul 19:101792. DOI: 10.1016/j.media.2020.101792
• dc.identifier.doi http://dx.doi.org/10.1016/j.media.2020.101792
• dc.identifier.issn 1361-8415
• dc.identifier.uri http://hdl.handle.net/10230/45146
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Medical Image Analysis. 2020 Jul 19:101792
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/DEP2013-44923- P
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2014-52923-R
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/642676
• dc.rights © Elsevier http://dx.doi.org/10.1016/j.media.2020.101792
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Confounder correctionen
• dc.subject.keyword Statistical shape analysisen
• dc.subject.keyword Computational anatomyen
• dc.subject.keyword Cardiac remodellingen
• dc.title Handling confounding variables in statistical shape analysis - application to cardiac remodellingen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion