A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography

Authors:
Yun Zhu;Xenophon Papademetris;Albert J. Sinusas;James S. Duncan
Affiliations:
Department of Biomedical Engineering, Yale University, USA;Department of Biomedical Engineering, Yale University, USA and Department of Diagnostic Radiology, Yale University, USA;Department of Diagnostic Radiology, Yale University, USA;Department of Biomedical Engineering, Yale University, USA and Department of Diagnostic Radiology, Yale University, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

Citing 5
Cited 2

Multilinear Analysis of Image Ensembles: TensorFaces

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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

Volumetric myocardial mechanics from 3D+t ultrasound data with multi-model tracking

STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
Complete valvular heart apparatus model from 4D cardiac CT

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

Real-time three-dimensional (RT3D) echocardiography is the newest generation of three-dimensional (3-D) echocardiography. Segmentation of RT3D echocardiographic images is essential for determining many important diagnostic parameters. In cardiac imaging, since the heart is a moving organ, prior knowledge regarding its shape and motion patterns becomes an important component for the segmentation task. However, most previous cardiac models 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 cardiac motion. In this paper, we present a subject-specific dynamical model (SSDM) which simultaneously handles inter-subject variability and cardiac dynamics (intra-subject variability). It can progressively predict the shape and motion patterns of a new sequence at the current frame based on the shapes observed in the past frames. The incorporation of this SSDM into the segmentation process is formulated in a recursive Bayesian framework. This results in a segmentation of each frame based on the intensity information of the current frame, as well as on the prediction from the previous frames. Quantitative results on 15 RT3D echocardiographic sequences show that automatic segmentation with SSDM is superior to that of either SM or GDM, and is comparable to manual segmentation.