Integrated segmentation and motion analysis of cardiac MR images using a subject-specific dynamical model

Abstract

In this paper we propose an integrated cardiac segmentation and motion tracking algorithm. First, we present a subject-specific dynamical model (SSDM) that simultaneously handles inter-subject variability and temporal dynamics (intra-subject variability), such that it can progressively identify the subject vector associated with a new cardiac sequence, and use this subject vector to predict the subject-specific segmentation of the future frames based on the shapes observed in earlier frames. Second, we use the segmentation as a guide in selecting feature points with significant shape characteristics, and invoke the Generalized Robust Point Matching (G-RPM) strategy with Boundary Element Method (BEM)-based regularization model to estimate physically realistic displacement field in a computationally efficient way. The integrated algorithm is formulated in a recursive Bayesian framework that sequentially segments cardiac images and estimates myocardial displacements. "Leave-one-out" validation on 32 sequences demonstrates that the segmentation results are improved when the SSDM is used, and the tracking results are much more accurate when the segmentation module is added.