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Statistical model-based segmentation of the left ventricles has received considerable attention these years. While many statistical models have been shown to improve segmentation results, most of them either belong to (1) static models (SM) that neglect the temporal coherence of a cardiac sequence, or (2) generic dynamical models (GDM) that neglect the individual differences of cardiac motion. In this paper, we propose a subject-specific dynamical model (SSDM) that can simultaneously handle inter-subject variability and temporal cardiac dynamics (intra-subject variability). We also design a dynamic prediction algorithm that can progressively predict the shape of a new cardiac sequence at a given frame based on the shapes observed in earlier frames. Furthermore, to reduce the accumulation of the segmentation errors throughout the entire sequence, we take into account the periodic nature of cardiac motion and perform bidirectional segmentation from a certain frame in a cardiac sequence. "Leave-one-out" validation on 32 sequences show that our algorithm can capture local shape variations and suppress the propagation of segmentation errors.

Bidirectional Segmentation of Three-Dimensional Cardiac MR Images Using a Subject-Specific Dynamical Model