The evaluation of single-view and multi-view fusion 3D echocardiography using image-driven segmentation and tracking

Real-time 3D echocardiography (RT3DE) promises a more objective and complete cardiac functional analysis by dynamic 3D image acquisition. Despite several efforts towards automation of left ventricle (LV) segmentation and tracking, these remain challenging research problems due to the poor-quality nature of acquired images usually containing missing anatomical information, speckle noise, and limited field-of-view (FOV). Recently, multi-view fusion 3D echocardiography has been introduced as acquiring multiple conventional single-view RT3DE images with small probe movements and fusing them together after alignment. This concept of multi-view fusion helps to improve image quality and anatomical information and extends the FOV. We now take this work further by comparing single-view and multi-view fused images in a systematic study. In order to better illustrate the differences, this work evaluates image quality and information content of single-view and multi-view fused images using image-driven LV endocardial segmentation and tracking. The image-driven methods were utilized to fully exploit image quality and anatomical information present in the image, thus purposely not including any high-level constraints like prior shape or motion knowledge in the analysis approaches. Experiments show that multi-view fused images are better suited for LV segmentation and tracking, while relatively more failures and errors were observed on single-view images.

[1]  Jonathan Chan,et al.  Left ventricular volume measurement with echocardiography: a comparison of left ventricular opacification, three-dimensional echocardiography, or both with magnetic resonance imaging. , 2008, European heart journal.

[2]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .

[3]  Paul Suetens,et al.  Three-Dimensional Cardiac Strain Estimation Using Spatio–Temporal Elastic Registration of Ultrasound Images: A Feasibility Study , 2008, IEEE Transactions on Medical Imaging.

[4]  William A Zoghbi,et al.  A randomized cross-over study for evaluation of the effect of image optimization with contrast on the diagnostic accuracy of dobutamine echocardiography in coronary artery disease The OPTIMIZE Trial. , 2008, JACC. Cardiovascular imaging.

[5]  L. Sugeng,et al.  Real-time 3-dimensional echocardiography: an integral component of the routine echocardiographic examination in adult patients? , 2009, Circulation.

[6]  Andrew Blake,et al.  Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography , 2009, FIMH.

[7]  J. Alison Noble,et al.  Adaptive Multiscale Ultrasound Compounding Using Phase Information , 2005, MICCAI.

[8]  J A Noble,et al.  OC18.01: Fusion of multiple four dimensional fetal echocardiography images can improve quality , 2009 .

[9]  Kashif Rajpoot,et al.  Multiview RT3D Echocardiography Image Fusion , 2009, FIMH.

[10]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Elsa D. Angelini,et al.  Segmentation of RT3D ultrasound with implicit deformable models without gradients , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

[12]  Marco Nolden,et al.  The Medical Imaging Interaction Toolkit , 2005, Medical Image Anal..

[13]  Boudewijn P. F. Lelieveldt,et al.  Left Ventricle Segmentation from Contrast Enhanced Fast Rotating Ultrasound Images Using Three Dimensional Active Shape Models , 2009, FIMH.

[14]  J. Weickert,et al.  Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods , 2005 .

[15]  Kashif Rajpoot,et al.  Local-phase based 3D boundary detection using monogenic signal and its application to real-time 3-D echocardiography images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[16]  Shunichi Homma,et al.  Feasibility of using a real-time 3-dimensional technique for contrast dobutamine stress echocardiography. , 2006, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[17]  Hervé Delingette,et al.  Functional Imaging and Modeling of the Heart, 5th International Conference, FIMH 2009, Nice, France, June 3-5, 2009. Proceedings , 2009, Functional Imaging and Modeling of the Heart.

[18]  J. Alison Noble,et al.  A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography , 2002, IEEE Transactions on Medical Imaging.

[19]  Shunichi Homma,et al.  Region-based endocardium tracking on real-time three-dimensional ultrasound. , 2009, Ultrasound in medicine & biology.

[20]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[21]  Jonathan Chan,et al.  Accuracy and feasibility of online 3-dimensional echocardiography for measurement of left ventricular parameters. , 2006, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[22]  Milan Sonka,et al.  Automatic segmentation of echocardiographic sequences by active appearance motion models , 2002, IEEE Transactions on Medical Imaging.

[23]  Dorin Comaniciu,et al.  Robust real-time myocardial border tracking for echocardiography: an information fusion approach , 2004, IEEE Transactions on Medical Imaging.

[24]  Peter Kovesi,et al.  Invariant measures of image features from phase information , 1996 .

[25]  Milan Sonka,et al.  3-D active appearance models: segmentation of cardiac MR and ultrasound images , 2002, IEEE Transactions on Medical Imaging.

[26]  N. de Jong,et al.  P2A-6 Automatic Segmentation of the Left Ventricle in 3D Echocardiography Using Active Appearance Models , 2007, 2007 IEEE Ultrasonics Symposium Proceedings.

[27]  K. Y. Esther Leung,et al.  Probabilistic framework for tracking in artifact-prone 3D echocardiograms , 2010, Medical Image Anal..

[28]  Andriy Myronenko,et al.  Maximum Likelihood Motion Estimation in 3D Echocardiography through Non-rigid Registration in Spherical Coordinates , 2009, FIMH.

[29]  J. Thomas,et al.  Left ventricular endocardial surface detection based on real-time 3D echocardiographic data. , 2001, European journal of ultrasound : official journal of the European Federation of Societies for Ultrasound in Medicine and Biology.

[30]  Elsa D. Angelini,et al.  Tracking of LV Endocardial Surface on Real-Time Three-Dimensional Ultrasound with Optical Flow , 2005, FIMH.

[31]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[32]  Carlos R. Castro-Pareja,et al.  Registration-assisted segmentation of real-time 3-D echocardiographic data using deformable models , 2005, IEEE Transactions on Medical Imaging.

[33]  William Stewart,et al.  Recommendations for chamber quantification. , 2006, European journal of echocardiography : the journal of the Working Group on Echocardiography of the European Society of Cardiology.

[34]  Victor Mor-Avi,et al.  Three-dimensional echocardiography: the benefits of the additional dimension. , 2006, Journal of the American College of Cardiology.

[35]  T. Marwick,et al.  Reproducibility and accuracy of echocardiographic measurements of left ventricular parameters using real-time three-dimensional echocardiography. , 2004, Journal of the American College of Cardiology.

[36]  Raj Shekhar,et al.  Fully automatic segmentation of left ventricular myocardium in real-time three-dimensional echocardiography , 2006, SPIE Medical Imaging.

[37]  Alessandro Sarti,et al.  Left ventricular volume estimation for real-time three-dimensional echocardiography , 2002, IEEE Transactions on Medical Imaging.

[38]  Marco Nolden,et al.  Interactive segmentation framework of the Medical Imaging Interaction Toolkit , 2009, Comput. Methods Programs Biomed..

[39]  Michael Unser,et al.  Myocardial motion analysis from B-mode echocardiograms , 2005, IEEE Transactions on Image Processing.

[40]  P. Allain,et al.  Comparison of fusion techniques for 3D+T echocardiography acquisitions from different acoustic windows , 2005, Computers in Cardiology, 2005.

[41]  Fredrik Orderud,et al.  Real-Time Active Shape Models for Segmentation of 3D Cardiac Ultrasound , 2007, CAIP.

[42]  Elsa D. Angelini,et al.  LV volume quantification via spatiotemporal analysis of real-time 3-D echocardiography , 2001, IEEE Transactions on Medical Imaging.

[43]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[44]  J. Alison Noble,et al.  2D+T acoustic boundary detection in echocardiography , 2000, Medical Image Anal..

[45]  James S. Duncan,et al.  Combinative Multi-scale Level Set Framework for Echocardiographic Image Segmentation , 2002, MICCAI.

[46]  J. Alison Noble,et al.  Registration of Multiview Real-Time 3-D Echocardiographic Sequences , 2007, IEEE Transactions on Medical Imaging.

[47]  A. Laine,et al.  Segmentation of real-time three-dimensional ultrasound for quantification of ventricular function: a clinical study on right and left ventricles. , 2005, Ultrasound in medicine & biology.

[48]  J. Alison Noble,et al.  Combining apical and parasternal views to improve motion estimation in real-time 3D echocardiographic sequences , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[49]  Fredrik Orderud,et al.  Real-Time Tracking of the Left Ventricle in 3D Echocardiography Using a State Estimation Approach , 2007, MICCAI.

[50]  Kashif Rajpoot,et al.  Image-Driven Cardiac Left Ventricle Segmentation for the Evaluation of Multiview Fused Real-Time 3-Dimensional Echocardiography Images , 2009, MICCAI.

[51]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[52]  H. Torp,et al.  3-D speckle tracking for assessment of regional left ventricular function. , 2009, Ultrasound in medicine & biology.

[53]  J. Bosch,et al.  Automated border detection in three-dimensional echocardiography: principles and promises. , 2010, European journal of echocardiography : the journal of the Working Group on Echocardiography of the European Society of Cardiology.

[54]  Richard B Devereux,et al.  Recommendations for chamber quantification: a report from the American Society of Echocardiography's Guidelines and Standards Committee and the Chamber Quantification Writing Group, developed in conjunction with the European Association of Echocardiography, a branch of the European Society of Cardio , 2005, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.