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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Yun Zhu , Departments of Biomedical Engineering and Diagnostic Radiology, Yale University, New Haven, CT 06520-8042, USA
Xenophon Papademetris , Departments of Biomedical Engineering and Diagnostic Radiology, Yale University, New Haven, CT 06520-8042, USA
Albert J. Sinusas , Departments of Biomedical Engineering and Diagnostic Radiology, Yale University, New Haven, CT 06520-8042, USA
James S. Duncan , Departments of Biomedical Engineering and Diagnostic Radiology, Yale University, New Haven, CT 06520-8042, USA
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.
CITATION
Yun Zhu, Xenophon Papademetris, Albert J. Sinusas, James S. Duncan, "Integrated segmentation and motion analysis of cardiac MR images using a subject-specific dynamical model", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563007
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