Segmenting the Papillary Muscles and the Trabeculae from High Resolution Cardiac CT through Restoration of Topological Handles

We introduce a novel algorithm for segmenting the high resolution CT images of the left ventricle (LV), particularly the papillary muscles and the trabeculae. High quality segmentations of these structures are necessary in order to better understand the anatomical function and geometrical properties of LV. These fine structures, however, are extremely challenging to capture due to their delicate and complex nature in both geometry and topology. Our algorithm computes the potential missing topological structures of a given initial segmentation. Using techniques from computational topology, e.g. persistent homology, our algorithm find topological handles which are likely to be the true signal. To further increase accuracy, these proposals are measured by the saliency and confidence from a trained classifier. Handles with high scores are restored in the final segmentation, leading to high quality segmentation results of the complex structures.

[1]  Junzhou Huang,et al.  4D Cardiac Reconstruction Using High Resolution CT Images , 2011, FIMH.

[2]  Gareth Funka-Lea,et al.  Automatic heart isolation for CT coronary visualization using graph-cuts , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[3]  J. Laidlaw,et al.  ANATOMY OF THE HUMAN BODY , 1967, The Ulster Medical Journal.

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Mark Jenkinson,et al.  Non-local Shape Descriptor: A New Similarity Metric for Deformable Multi-modal Registration , 2011, MICCAI.

[6]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[7]  Olivier Ecabert,et al.  Automatic Model-Based Segmentation of the Heart in CT Images , 2008, IEEE Transactions on Medical Imaging.

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

[9]  Herbert Edelsbrunner,et al.  Computational Topology - an Introduction , 2009 .

[10]  M. Breeuwer,et al.  Detection of trabeculae and papillary muscles in cardiac MR images , 2005, Computers in Cardiology, 2005.

[11]  David F Yankelevitz,et al.  Left ventricular papillary muscles and trabeculae are significant determinants of cardiac MRI volumetric measurements: effects on clinical standards in patients with advanced systolic dysfunction. , 2008, International journal of cardiology.

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

[13]  Max A. Viergever,et al.  Multi-Atlas-Based Segmentation With Local Decision Fusion—Application to Cardiac and Aortic Segmentation in CT Scans , 2009, IEEE Transactions on Medical Imaging.

[14]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  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.

[16]  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.

[17]  Richard D. White,et al.  CT of the heart: principles, advances, clinical uses. , 2005, Cleveland Clinic journal of medicine.

[18]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[19]  Dimitris N. Metaxas,et al.  3D Cardiac Anatomy Reconstruction Using High Resolution CT Data , 2004, MICCAI.

[20]  Scott Kulp,et al.  Using High Resolution Cardiac CT Data to Model and Visualize Patient-Specific Interactions between Trabeculae and Blood Flow , 2011, MICCAI.

[21]  Christoph H. Lampert,et al.  Enforcing topological constraints in random field image segmentation , 2011, CVPR 2011.

[22]  Bruce Fischl,et al.  Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops , 2007, IEEE Transactions on Medical Imaging.

[23]  David Cohen-Steiner,et al.  Stability of Persistence Diagrams , 2005, Discret. Comput. Geom..

[24]  Dinggang Shen,et al.  An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures , 2001, IEEE Transactions on Medical Imaging.

[25]  Pierre Hellier,et al.  Level Set Methods in an EM Framework for Shape Classification and Estimation , 2004, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[26]  Sebastian Nowozin,et al.  Global connectivity potentials for random field models , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Herbert Edelsbrunner,et al.  The Adaptive Topology of a Digital Image , 2012, 2012 Ninth International Symposium on Voronoi Diagrams in Science and Engineering.

[28]  Anthony J. Yezzi,et al.  Global Regularizing Flows With Topology Preservation for Active Contours and Polygons , 2007, IEEE Transactions on Image Processing.