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Abstract #1172

Automatic Segmentation of Short-Axis Cardiac MRI using a Biventricular Deformable Model with an Explicit Thickness Prior

Paul A. Yushkevich1, Hui Sun1, Federico M. Sukno2, Catalina Tobon-Gomez2, Hongzhi Wang1, Alejandro F. Frangi2

1PICSL, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States; 2CISTIB, Universitat Pompeu Fabra, CIBER-BBN, Barcelona, Spain

This new approach overcomes some of the challenges in automatic cardiac MRI segmentation of by modeling the myocardium using a deformable model that explicitly describes the skeleton of the myocardium and its thickness. This geometrical model is coupled with an appearance model and statistical shape prior learned from training data. The proposed approach achieves high accuracy compared to manual segmentation on end-diastole MR images of patients with common pathologies.