Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images

Abstract : Statistical shape models (SSMs) represent a powerful tool used in patient-specific modeling to segment medical images because they incorporate a-priori knowledge that guide the model during deformation. Our aim was to evaluate segmentation accuracy in terms of left ventricular (LV) volumes obtained using four different SSMs versus manual gold standard tracing on cardiac magnetic resonance (CMR) images. A database of 3D echocardiographic (3DE) LV surfaces obtained in 435 patients was used to generate four different SSMs, based on cardiac phase selection. Each model was scaled and deformed to detect LV endocardial contours in the enddiastolic (ED) and end-systolic (ES) frames of a CMR short-axis (SAX) stack for 15 patients with normal LV function. Linear correlation and Bland–Altman analyses versus gold-standard showed in all cases high correlation (r²>0.95), non-significant biases and narrow limits of agreement.
Type de document :
Communication dans un congrès
Computing in Cardiology, Sep 2015, Nice, France
Liste complète des métadonnées

https://hal.inria.fr/hal-01244196
Contributeur : Mark Potse <>
Soumis le : mardi 15 décembre 2015 - 14:55:29
Dernière modification le : mercredi 29 novembre 2017 - 10:29:55

Identifiants

  • HAL Id : hal-01244196, version 1

Collections

Citation

Concetta Piazzese, M. Chiara Carminati, Andrea Colombo, Rolf Krause, Mark Potse, et al.. Evaluation of different statistical shape models for segmentation of the left ventricular endocardium from magnetic resonance images. Computing in Cardiology, Sep 2015, Nice, France. 〈hal-01244196〉

Partager

Métriques

Consultations de la notice

31