Improved Estimation of Cardiac Function Parameters Using a Combination of Independent Automated Segmentation Results in Cardiovascular Magnetic Resonance Imaging.
Fiche publication
Date publication
janvier 2015
Journal
PloS one
Auteurs
Membres identifiés du Cancéropôle Est :
Pr COCHET Alexandre
Tous les auteurs :
Lebenberg J, Lalande A, Clarysse P, Buvat I, Casta C, Cochet A, Constantinidès C, Cousty J, de Cesare A, Jehan-Besson S, Lefort M, Najman L, Roullot E, Sarry L, Tilmant C, Frouin F, Garreau M
Lien Pubmed
Résumé
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 biomarker 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 different automated 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.
Mots clés
Algorithms, Humans, Image Enhancement, methods, Image Interpretation, Computer-Assisted, methods, Magnetic Resonance Imaging, Cine, methods, Pattern Recognition, Automated, methods, Reproducibility of Results, Stroke Volume, physiology, Ventricular Function, Left, physiology
Référence
PLoS ONE. 2015 ;10(8):e0135715