Automatic image‐driven segmentation of the ventricles in cardiac cine MRI

To propose and to evaluate a novel method for the automatic segmentation of the heart's two ventricles from dynamic (“cine”) short‐axis “steady state free precession” (SSFP) MR images. This segmentation task is of significant clinical importance. Previously published automated methods have various disadvantages for routine clinical use.

[1]  S. Plein,et al.  Normal human left and right ventricular dimensions for MRI as assessed by turbo gradient echo and steady‐state free precession imaging sequences , 2003, Journal of magnetic resonance imaging : JMRI.

[2]  Kieran Clarke,et al.  Determination of cardiac volumes and mass with FLASH and SSFP cine sequences at 1.5 vs. 3 Tesla: A validation study , 2006, Journal of magnetic resonance imaging : JMRI.

[3]  J. Reiber,et al.  Comparison between manual and semiautomated analysis of left ventricular volume parameters from short-axis MR images. , 1997, Journal of computer assisted tomography.

[4]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[5]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[6]  W. J. Hedley,et al.  Left ventricular mass and volume: fast calculation with guide-point modeling on MR images. , 2000, Radiology.

[7]  Daniel Rueckert,et al.  Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm , 2004, Medical Image Anal..

[8]  Hervé Delingette,et al.  An electromechanical model of the heart for image analysis and simulation , 2006, IEEE Transactions on Medical Imaging.

[9]  Jens von Berg,et al.  Automated Segmentation of the Left Ventricle in Cardiac MRI , 2003, MICCAI.

[10]  Jens von Berg,et al.  Coupled deformable models with spatially varying features for quantitative assessment of left ventricular function from cardiac MRI , 2003, SPIE Medical Imaging.

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

[12]  D. Pennell,et al.  Breath-hold FLASH and FISP cardiovascular MR imaging: left ventricular volume differences and reproducibility. , 2002, Radiology.

[13]  Boudewijn P F Lelieveldt,et al.  Evaluation of a new method for automated detection of left ventricular boundaries in time series of magnetic resonance images using an Active Appearance Motion Model. , 2004, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[14]  Marie-Pierre Jolly,et al.  Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images , 2006, International Journal of Computer Vision.

[15]  Daniel Rueckert,et al.  Spatial transformation of motion and deformation fields using nonrigid registration , 2004, IEEE Transactions on Medical Imaging.

[16]  David T. Gering,et al.  Automatic Segmentation of Cardiac MRI , 2003, MICCAI.

[17]  S. Plein,et al.  Steady‐state free precession magnetic resonance imaging of the heart: Comparison with segmented k‐space gradient‐echo imaging , 2001, Journal of magnetic resonance imaging : JMRI.

[18]  Juha Koikkalainen,et al.  Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images , 2004, Medical Image Anal..

[19]  Amol S Pednekar,et al.  Automatic identification of the left ventricle in cardiac cine‐MR images: Dual‐contrast cluster analysis and scout‐geometry approaches , 2006, Journal of magnetic resonance imaging : JMRI.

[20]  S. Plein,et al.  Comparison of right ventricular volume measurements between axial and short axis orientation using steady‐state free precession magnetic resonance imaging , 2003, Journal of magnetic resonance imaging : JMRI.

[21]  Maxime Sermesant,et al.  Cardiac Function Estimation from MRI Using a Heart Model and Data Assimilation: Advances and Difficulties , 2005, FIMH.

[22]  Damini Dey,et al.  Automated image registration of gated cardiac single‐photon emission computed tomography and magnetic resonance imaging , 2004, Journal of magnetic resonance imaging : JMRI.

[23]  Hervé Delingette,et al.  Deformable biomechanical models: Application to 4D cardiac image analysis , 2003, Medical Image Anal..

[24]  David Levin,et al.  Techniques for efficient, real-time, 3D visualization of multi-modality cardiac data using consumer graphics hardware. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[25]  Boudewijn P. F. Lelieveldt,et al.  Time Continuous Tracking and Segmentation of Cardiovascular Magnetic Resonance Images Using Multidimensional Dynamic Programming , 2006, Investigative radiology.

[26]  A. Ardeshir Goshtasby,et al.  Segmentation of cardiac cine MR images for extraction of right and left ventricular chambers , 1995, IEEE Trans. Medical Imaging.

[27]  Paul F. Whelan,et al.  Automatic segmentation of the left ventricle cavity and myocardium in MRI data , 2006, Comput. Biol. Medicine.

[28]  Piotr A Wielopolski,et al.  Automatic quantitative left ventricular analysis of cine MR images by using three-dimensional information for contour detection. , 2006, Radiology.

[29]  Daniel Rueckert,et al.  Knowledge-based tensor anisotropic diffusion of cardiac magnetic resonance images , 1999, Medical Image Anal..

[30]  Milan Sonka,et al.  Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images , 2001, IEEE Transactions on Medical Imaging.

[31]  Marcel Breeuwer,et al.  Automatic Contour Propagation in Cine Cardiac Magnetic Resonance Images , 2006, IEEE Transactions on Medical Imaging.

[32]  Thomas Netsch,et al.  Model-based segmentation of cardiac MRI cine sequences: a Bayesian formulation , 2004, SPIE Medical Imaging.