Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging

Abstract : This work aimed at combining different segmentation approaches to produce a robust and accurate segmentation result. Three to five segmentation results of the left ventricle were combined using the STAPLE algorithm and the reliability of the resulting segmentation was evaluated in comparison with the result of each individual segmentation method. This comparison was performed using a supervised approach based on a reference method. Then, we used an unsupervised statistical evaluation, the extended Regression Without Truth (eRWT) that ranks different methods according to their accuracy in estimating a specific bio-marker in a population. The segmentation accuracy was evaluated by estimating six cardiac function parameters resulting from the left ventricle contour delineation using a public cardiac cine MRI database. Eight different segmentation methods, including three expert delineations and five automated methods, were considered, and sixteen combinations of the automated methods using STAPLE were investigated. The supervised and unsupervised evaluations demonstrated that in most cases, STAPLE results provided better estimates than individual automated segmentation methods. Overall, combining differentautomated segmentation methods improved the reliability of the segmentation result compared to that obtained using an individual method and could achieve the accuracy of an expert.
Type de document :
Article dans une revue
PLoS ONE, Public Library of Science, 2015, 10 (8), pp.e0135715. <10.1371/journal.pone.0135715>
Liste complète des métadonnées


http://hal.upmc.fr/hal-01235953
Contributeur : Gestionnaire Hal-Upmc <>
Soumis le : mardi 1 décembre 2015 - 09:32:28
Dernière modification le : mercredi 19 avril 2017 - 13:20:09
Document(s) archivé(s) le : vendredi 28 avril 2017 - 23:29:07

Fichier

journal.pone.0135715.pdf
Publication financée par une institution

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Jessica Lebenberg, Alain Lalande, Patrick Clarysse, Irene Buvat, Christopher Casta, et al.. Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging. PLoS ONE, Public Library of Science, 2015, 10 (8), pp.e0135715. <10.1371/journal.pone.0135715>. <hal-01235953>

Partager

Métriques

Consultations de
la notice

639

Téléchargements du document

136