Enhanced semi-automated method to identify the endo-cardium and epi-cardium borders

We present two semi-automatic solution methods for the three dimensional (3D) segmentation of cavity and myocardium from a 3D cardiac multislice CT (MSCT) data. The main framework of the segmentation algorithms is based on random walks, in which the novelty lies in a seeds-selection method composed of region growing technique and morphological operation to locate and identify the cavity and myocardium of the left ventricle (LV). In the first solution, a semi-automatic segmentation approach (Method_1) is suggested to extract the epi-cardium and endo-cardium boundaries of LV of the heart. This proposed solution depends on the use of the normal random walker algorithm. In the second solution, a semi-automatic segmentation approach (Method_2) based on improved random walks is proposed. Segmentation is done within the framework of toboggan algorithm in combination with a random walk based technique. The two proposed semi-automatic segmentation methods either based on the normal random walker or the improved random walker algorithms utilized six-connected lattice topology and a conjugate gradient method to promote the segmentation performance of the 3D data. The two semi-automatic solution methods were evaluated using 20 cardiac MSCT datasets. Semi-automatic generated contours were compared to expert contours. For Method_1, 83.4% of epi-cardial contours and 74.7% of endo-cardial contours had a maximum error of 5 mm along 95% of the contour arc length. For Method_2, those numbers were 94.3% (epi-cardium) and 92.3% (endo-cardium), respectively. Volume regression analysis revealed good linear correlations between manual and semiautomatic volumes, r ≥ 0.99.

[1]  S. Allender,et al.  European cardiovascular disease statistics , 2008 .

[2]  Paul F. Whelan,et al.  Segmentation of the Left Ventricle of the Heart in 3-D+t MRI Data Using an Optimized Nonrigid Temporal Model , 2008, IEEE Transactions on Medical Imaging.

[3]  Gustavo Carneiro,et al.  Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree , 2008, IEEE Transactions on Medical Imaging.

[4]  L. Boxt.  CT Anatomy of the heart , 2005, The International Journal of Cardiovascular Imaging.

[5]  H.G. Torp,et al.  Automatic coupled segmentation of endo- and epicardial borders in 3D echocardiography , 2008, 2008 IEEE Ultrasonics Symposium.

[6]  King-Sun Fu,et al.  A survey on image segmentation , 1981, Pattern Recognit..

[7]  Marleen de Bruijne,et al.  Shape Particle Filtering for Image Segmentation , 2004, MICCAI.

[8]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[9]  Yi-Ping Hung,et al.  Comparison between immersion-based and toboggan-based watershed image segmentation , 2006, IEEE Transactions on Image Processing.

[10]  Milan Sonka,et al.  Segmentation of intravascular ultrasound images: a knowledge-based approach , 1995, IEEE Trans. Medical Imaging.

[11]  C. Lamberti,et al.  Maximum likelihood segmentation of ultrasound images with Rayleigh distribution , 2005, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[12]  Michael Unser,et al.  Variational B-Spline Level-Set: A Linear Filtering Approach for Fast Deformable Model Evolution , 2009, IEEE Transactions on Image Processing.

[13]  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.

[14]  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.

[15]  T. Todd Elvins,et al.  Visualization of 3D ultrasound data , 1993, IEEE Computer Graphics and Applications.

[16]  Leo Grady,et al.  A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[17]  J. Fairfield,et al.  Toboggan contrast enhancement for contrast segmentation , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

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

[19]  J G Goldin,et al.  Left ventricular ejection fraction using 64-slice CT coronary angiography and new evaluation software: initial experience. , 2008, The British journal of radiology.

[20]  Frank Harary,et al.  Graph Theory , 2016 .

[21]  Jorge S. Marques,et al.  Robust Shape Tracking With Multiple Models in Ultrasound Images , 2008, IEEE Transactions on Image Processing.

[22]  Edward A. Geiser,et al.  An Effective Algorithm for Extracting Serial Endocardial Borders from 2-Dimensional Echocardiograms , 1984, IEEE Transactions on Biomedical Engineering.

[23]  Charles Kervrann,et al.  Statistical deformable model-based segmentation of image motion , 1999, IEEE Trans. Image Process..

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

[25]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[26]  Rachid Deriche,et al.  Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation , 2002, International Journal of Computer Vision.

[27]  Gareth Funka-Lea,et al.  Multi-label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials , 2004, ECCV Workshops CVAMIA and MMBIA.

[28]  Robert Allen,et al.  Handbook of Medical Imaging—Processing and Analysis , 2001 .

[29]  Osman Ratib,et al.  Quantitative analysis of cardiac function , 2000 .

[30]  Dorin Comaniciu,et al.  Database-guided segmentation of anatomical structures with complex appearance , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[31]  Timothy F. Cootes,et al.  A Unified Framework for Atlas Matching Using Active Appearance Models , 1999, IPMI.

[32]  Richard Barrett,et al.  Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods , 1994, Other Titles in Applied Mathematics.

[33]  Shuo Li,et al.  Embedding Overlap Priors in Variational Left Ventricle Tracking , 2009, IEEE Transactions on Medical Imaging.

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

[35]  L. Qingsheng,et al.  A New Fast Random Walk Segmentation Algorithm , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[36]  C. Cavaro-Ménard,et al.  Simultaneous and correlated detection of endocardial and epicardial borders on short-axis MR images for the measurement of left ventricular mass. , 1998, Radiographics : a review publication of the Radiological Society of North America, Inc.

[37]  J. Dodziuk.  Difference equations, isoperimetric inequality and transience of certain random walks , 1984 .

[38]  Jianhua Xuan,et al.  Segmentation of magnetic resonance brain image: integrating region growing and edge detection , 1995, Proceedings., International Conference on Image Processing.

[39]  Peter G. Doyle,et al.  Random Walks and Electric Networks: REFERENCES , 1987 .

[40]  Gustavo Carneiro,et al.  Robust left ventricle segmentation from ultrasound data using deep neural networks and efficient search methods , 2010, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[41]  J P Roth,et al.  AN APPLICATION OF ALGEBRAIC TOPOLOGY TO NUMERICAL ANALYSIS: ON THE EXISTENCE OF A SOLUTION TO THE NETWORK PROBLEM. , 1955, Proceedings of the National Academy of Sciences of the United States of America.

[42]  Gustavo Carneiro,et al.  Multiple dynamic models for tracking the left ventricle of the heart from ultrasound data using particle filters and deep learning architectures , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[43]  Leo Grady,et al.  Weights and Topology: A Study of the Effects of Graph Construction on 3D Image Segmentation , 2008, MICCAI.

[44]  N. Paragios.  A level set approach for shape-driven segmentation and tracking of the left ventricle , 2003, IEEE Transactions on Medical Imaging.

[45]  Dorin Comaniciu,et al.  Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features , 2008, IEEE Transactions on Medical Imaging.

[46]  W A Kalender,et al.  System performance of multislice spiral computed tomography. , 2000, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[47]  William D. Ross,et al.  Left Ventricular Endocardial and Epicardial Border Length Delineation with Perflutren Contrast during Transthoracic Echocardiography , 2011, Echocardiography.

[48]  Leo Grady,et al.  Random Walks for Image Segmentation , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  E. Schwartz,et al.  Space-variant computer vision: a graph-theoretic approach , 2004 .

[50]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[51]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[52]  Leon Axel,et al.  Semiautomated Segmentation of Myocardial Contours for Fast Strain Analysis in Cine Displacement-Encoded MRI , 2008, IEEE Transactions on Medical Imaging.

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

[54]  Xu Yao,et al.  Fast image segmentation by sliding in the derivative terrain , 1992, Other Conferences.