A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Multilinear Independent Components Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Segmenting and tracking the left ventricle by learning the dynamics in cardiac images
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Subject specific shape modeling with incremental mixture models
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
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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.