Nonsupervised ranking of different segmentation approaches: application to the estimation of the left ventricular ejection fraction from cardiac cine MRI sequences.
A statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eight methods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical distribution of the parameter of interest in the database. The ranking of the methods relies on figures of merit derived from the regression and computed using a bootstrap process. The methodology was applied to the estimation of the left ventricular ejection fraction derived from cardiac magnetic resonance images segmented using eight approaches with different degrees of automation: three segmentations were entirely manually performed and the others were variously automated. The ranking of methods was consistent with the expected performance of the estimation methods: the most accurate estimates of the ejection fraction were obtained using manual segmentations. The robustness of the ranking was demonstrated when at least three methods were compared. These results suggest that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available.
LIF, INSERM UMR_S 678 Université Pierre et Marie Curie, 75013 Paris, France. jessica.lebenberg@gmail.com
Jessica
J
Lebenberg
Irène
I
Buvat
Alain
A
Lalande
Patrick
P
Clarysse
Christopher
C
Casta
Alexandre
A
Cochet
Constantin
C
Constantinides
Jean
J
Cousty
Alain
A
de Cesare
Stéphanie
S
Jehan-Besson
Muriel
M
Lefort
Laurent
L
Najman
Elodie
E
Roullot
Laurent
L
Sarry
Christophe
C
Tilmant
Mireille
M
Garreau
Frédérique
F
Frouin
D016000:Cluster Analysis; D006321:Heart / Q000033:anatomy & histology / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D012044:Regression Analysis; D013318:Stroke Volume / Q000502:physiology*; D016277:Ventricular Function, Left / Q000502:physiology*