EPICARDIAL SEGMENTATION IN DYNAMIC CARDIAC MR SEQUENCES USING PRIORS ON SHAPE, INTENSITY, AND DYNAMICS, IN A LEVEL SET FRAMEWORK

We propose a method for epicardial segmentation in dynamic MR sequences using priors on shape, intensity, and dynamics. The prior models are built from a training set of manually segmented sequences. The dynamics of the heart and background intensities are modeled with linear autoregressive models whose parameters are learnt from training data and then used as priors for segmentation purposes. The segmented training shapes are registered with respect to a common reference. Each registered shape is represented with a signed distance function, and a statistical shape model is learnt using PCA on these functions. Segmentation is achieved by minimizing a spatial-temporal generalization of the Mumford-Shah energy functional in a level set framework