A novel approach for accurate estimation of left ventricle global indexes from short-axis cine MRI

A new automatic approach for the estimation of global indexes from short-axis cine cardiac magnetic resonance (CMR) images is proposed. The inner contour of the left ventricle (LV) is segmented with a level set-based deformable model. Its evolution is controlled by a specially designed stochastic speed function that accounts for a learned spatially variant statistical shape prior, a 1^st-order visual appearance descriptor of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2^nd-order homogeneity descriptor. After the inner contour delineation, the total cavity volume (time varying LV volume) data is used to estimate the LV global functional indexes, i.e., ejection fraction, systolic and diastolic slopes. Experiments with in-vivo CMR data, obtained from subjects with chronic ischemic heart disease and damage that is documented by viability MRI, confirm a high robustness and accuracy of the proposed approach.