Automatic Model-Based Segmentation of the Heart in CT Images

Automatic image processing methods are a pre-requisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations. This improves convergence as well as segmentation accuracy. The heart is first localized in the image using a 3-D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multicompartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patient's anatomy. A mean surface-to-surface error of 0.82 mm was measured in a leave-one-out quantitative validation carried out on 28 images. Moreover, the piecewise affine transformation introduced for mesh initialization and adaptation shows better interphase and interpatient shape variability characterization than commonly used principal component analysis.

[1]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[2]  Philip E. Gill,et al.  Practical optimization , 1981 .

[3]  E L Ritman,et al.  Extraction of left-ventricular chamber from 3-D CT images of the heart. , 1990, IEEE transactions on medical imaging.

[4]  C. Taylor,et al.  Active shape models - 'Smart Snakes'. , 1992 .

[5]  J. Cauvin,et al.  3D modeling in myocardial 201TL SPECT. , 1993, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[6]  Timothy F. Cootes,et al.  The Use of Active Shape Models for Locating Structures in Medical Images , 1993, IPMI.

[7]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[8]  Demetri Terzopoulos,et al.  A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis. , 1995, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[9]  James S. Duncan,et al.  Model-based deformable surface finding for medical images , 1996, IEEE Trans. Medical Imaging.

[10]  Akshay K. Singh,et al.  Deformable models in medical image analysis , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[11]  Demetri Terzopoulos,et al.  Deformable models in medical image analysis: a survey , 1996, Medical Image Anal..

[12]  Milan Sonka,et al.  Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples , 2000, IEEE Transactions on Medical Imaging.

[13]  Alejandro F. Frangi,et al.  Three-dimensional modeling for functional analysis of cardiac images, a review , 2001, IEEE Transactions on Medical Imaging.

[14]  Jürgen Weese,et al.  Shape Constrained Deformable Models for 3D Medical Image Segmentation , 2001, IPMI.

[15]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  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.

[17]  Timothy F. Cootes,et al.  A minimum description length approach to statistical shape modeling , 2002, IEEE Transactions on Medical Imaging.

[18]  Daniel Rueckert,et al.  Atlas-Based Segmentation and Tracking of 3D Cardiac MR Images Using Non-rigid Registration , 2002, MICCAI.

[19]  Alejandro F. Frangi,et al.  Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling , 2002, IEEE Transactions on Medical Imaging.

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

[21]  C. Claussen,et al.  Non-invasive coronary angiography with high resolution multidetector-row computed tomography. Results in 102 patients. , 2002, European heart journal.

[22]  Alejandro F. Frangi,et al.  Automatic construction of multiple-object three-dimensional shape models: Application to cardiac modeling , 2002 .

[23]  Daniel Rueckert,et al.  Segmentation of 4D Cardiac MR Images Using a Probabilistic Atlas and the EM Algorithm , 2003, MICCAI.

[24]  Jürgen Weese,et al.  Automated 3-D PDM construction from segmented images using deformable models , 2003, IEEE Transactions on Medical Imaging.

[25]  Paramate Horkaew,et al.  Optimal Deformable Surface Models for 3D Medical Image Analysis , 2003, IPMI.

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

[27]  Olivier Ecabert,et al.  Adaptive Hough transform for the detection of natural shapes under weak affine transformations , 2004, Pattern Recognit. Lett..

[28]  Jürgen Weese,et al.  Automated segmentation of the left ventricle in cardiac MRI , 2004, Medical Image Anal..

[29]  S. Ordas,et al.  Automatic Quantitative Analysis of Myocardial Wall Motion and Thickening from Long-and Short-Axis Cine MRI Studies , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[30]  Rüdiger Dillmann,et al.  Automatic segmentation of the left ventricle and computation of diagnostic parameters using regiongrowing and a statistical model , 2005, SPIE Medical Imaging.

[31]  Richard A. Robb,et al.  Semiautomatic segmentation of the heart from CT images based on intensity and morphological features , 2005, SPIE Medical Imaging.

[32]  Cristian Lorenz,et al.  Multi-surface Cardiac Modelling, Segmentation, and Tracking , 2005, FIMH.

[33]  O. Ecabert,et al.  Feature optimization via simulated search for model-based heart segmentation , 2005 .

[34]  Johan Montagnat,et al.  4D deformable models with temporal constraints: application to 4D cardiac image segmentation , 2005, Medical Image Anal..

[35]  Mikkel B. Stegmann,et al.  Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation , 2005, SPIE Medical Imaging.

[36]  J. Weese,et al.  Towards automatic full heart segmentation in computed-tomography images , 2005, Computers in Cardiology, 2005.

[37]  Jeroen J. Bax,et al.  Assessment of global and regional left ventricular function and volumes with 64-slice MSCT: A comparison with 2D echocardiography , 2006, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[38]  Olivier Ecabert,et al.  Modeling shape variability for full heart segmentation in cardiac computed-tomography images , 2006, SPIE Medical Imaging.

[39]  Cristian Lorenz,et al.  A comprehensive shape model of the heart , 2006, Medical Image Anal..

[40]  Rüdiger Dillmann,et al.  Segmentation of the left and right cardiac ventricle using a combined bi-temporal statistical model , 2006, SPIE Medical Imaging.

[41]  Olivier Ecabert,et al.  Discriminative boundary detection for model-based heart segmentation in CT images , 2007, SPIE Medical Imaging.