Boundary element method-based regularization for recovering of LV deformation

Yan, Ping; Sinusas, Albert J.; Duncan, James S.

doi:10.1016/j.media.2007.04.007

Cited by 32 publications

(23 citation statements)

References 21 publications

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“…There have been a number of efforts in LV deformation analysis [1,2,3,4], of which one important category is to use shape-based tracking algorithm to find the correspondence of the endocardial (ENDO) and epicardial (EPI) borders in two adjacent frames, followed by the dense correspondence interpolation within the myocardium using a biomechanical model. To find the ENDO-and EPI boundaries, Shi used the manual segmentation of the endocardium and epicardium by experts [1].…”

Section: Introductionmentioning

confidence: 99%

“…Lin eliminated the need for manual segmentation by using a Canny edge detector for rough segmentation, followed by the Generalized Robust Point Matching (GRPM) algorithm to find feature point correspondence [2]. Yan extended their work by introducing the Boundary Element Method (BEM) that greatly improves the computational efficiency of the biomechanical model [3].…”

Section: Introductionmentioning

confidence: 99%

“…We do not use segmented boundaries themselves to estimate displacements, thus reducing the dependency on the accuracy of the segmentation. We formulate our approach in a recursive Bayesian framework that segments the myocardium, extracts feature points from the narrowband of segmented contours, and estimates a dense displacement field within myocardium using the BEM-GRPM algorithm [3]. The estimated displacement field is then used to calculate the myocardial strains over multiple frames.…”

Section: Introductionmentioning

confidence: 99%

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Integrated segmentation and deformation analysis of 4-D cardiac MR images

Zhu

Yan

Papademetris

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Segmentation and motion estimation from cardiac images are usually considered separately, yet they can obviously benefit from each other. In this paper, we propose a joint segmentation and motion estimation algorithm for the purposes of myocardial deformation analysis and strain estimation. We use segmentation as a guide for selecting feature points with significant shape characteristics, and invoke a Generalized Robust Point Matching (GRPM) strategy with Boundary Element Method (BEM)-based regularization model to estimate the dense displacement field and strain map from 3-D cardiac sequences. Quantitative analysis of the results is performed in comparison with the displacements found using implanted markers, taken to be gold standards.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated segmentation and deformation analysis of 4-D cardiac MR images

Zhu

Yan

Papademetris

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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“…Among all computer vision-based techniques, one important category is to use shape-based tracking algorithm in order to find the correspondence of the myocardial 1 This work is supported by the grant 5R01HL082640-02. boundaries in two adjacent frames, followed by the dense correspondence interpolation within the myocardium using a biomechanical model [11,6,14]. To find the endocardial (ENDO)-and epicardial (EPI) boundaries, Shi used the manual segmentation by experts [11].…”

Section: Introductionmentioning

confidence: 99%

“…Lin eliminated the need for manual segmentation by using a Canny edge detector for rough segmentation, followed by the Generalized Robust Point Matching (G-RPM) algorithm to find the correspondence of feature points [6]. Yan extended the work by introducing the Boundary Element Method (BEM) that greatly reduces the computational expense of the biomechanical model [14].…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Papademetris

Sinusas

et al. 2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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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.

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