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Statistical models have shown considerable promise as a basis for segmenting and interpreting cardiac images. While a variety of statistical models have been proposed to improve the segmentation results, most of them are either static models (SMs), which neglect the temporal dynamics of a cardiac sequence, or generic dynamical models (GDMs), which are homogeneous in time and neglect the intersubject...
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...
Statistical model-based segmentation of the left ventricle from cardiac images has received considerable attention in recent years. While a variety of statistical models have been shown to improve segmentation results, most of them are either static models (SM) which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM) which neglect the inter-subject variability of...
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