Data-driven shape parameterization for segmentation of the right ventricle from 3D+t echocardiography

Medical Image Analysis

Volumn 21, Issue 1, 2015, Pages 29-39

  Stebbing, Richard V ^a   Namburete, Ana I L ^a   Upton, Ross ^a   Leeson, Paul ^a   Noble, J Alison ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Linear basis shape model; Segmentation; Subdivision surfaces

Indexed keywords

ECHOCARDIOGRAPHY;

ACCURATE MEASUREMENT; DENSE CORRESPONDENCES; GEOMETRIC PROPERTIES; INCOMPLETE BOUNDARIES; MODEL-BASED SEGMENTATION; SHAPE MODEL; SHAPE PARAMETERIZATION; SUBDIVISION SURFACES;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; HEART RIGHT VENTRICLE; IMAGE ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REAL TIME ULTRASOUND SCANNER; THREE DIMENSIONAL ECHOCARDIOGRAPHY; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; ECHOGRAPHY; EVALUATION STUDY; FOUR DIMENSIONAL COMPUTED TOMOGRAPHY; HEART RIGHT VENTRICLE FUNCTION; HEART VENTRICLE; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; VALIDATION STUDY;

ALGORITHMS; COMPUTER SIMULATION; FOUR-DIMENSIONAL COMPUTED TOMOGRAPHY; HEART VENTRICLES; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, CARDIOVASCULAR; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; VENTRICULAR DYSFUNCTION, RIGHT;

EID: 84922419202     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2014.12.002     Document Type: Article

References (41)

1. Agarwal, S., Mierle, K., et al., 2010. Ceres solver. http://www.ceres-solver.org.
2. Amestoy P.R., Davis T.A., Duff I.S. An approximate minimum degree ordering algorithm. SIAM J. Matrix Anal. Appl. 1996, 17:886-905.
3. Barbosa D., Dietenbeck T., Schaerer J., D'hooge J., Friboulet D., Bernard O. B-spline explicit active surfaces: an efficient framework for real-time 3-D region-based segmentation. IEEE TIP 2012, 21:241-251.
4. Barbosa D., Dietenbeck T., Heyde B., Houle H., Friboulet D., D'hooge J., Bernard O. Fast and fully automatic 3-D echocardiographic segmentation using B-spline explicit active surfaces: feasibility study and validation in a clinical setting. Ultrasound Med. Biol. 2013, 39:89-101.
5. Blake A., Isard M., et al. Active Contours 2000, vol. 1. Springer.
6. Bosch J.G., Mitchell S.C., Lelieveldt B.P., Nijland F., Kamp O., Sonka M., Reiber J.H. Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE TMI 2002, 21:1374-1383.
7. Brett A.D., Taylor C.J. A method of automated landmark generation for automated 3D PDM construction. Image Vis. Comput. 2000, 18:739-748.
8. Caselles V., Kimmel R., Sapiro G. Geodesic active contours. IJCV 1997, 22:61-79.
9. Cashman T.J., Fitzgibbon A.W. What shape are dolphins? Building 3D morphable models from 2D images. IEEE TPAMI 2013, 35:232-244.
10. Caudron J., Fares J., Lefebvre V., Vivier P., Petitjean C., Dacher J. Cardiac MRI assessment of right ventricular function in acquired heart disease: factors of variability. Acad. Radiol. 2012, 19:991-1002.
11. Chen Y., Davis T.A., Hager W.W., Rajamanickam S. Algorithm 887: CHOLMOD, supernodal sparse Cholesky factorization and update/downdate. ACM TOMS 2008, 35:22.
12. Cootes T.F., Taylor C.J., Cooper D.H., Graham J. Active shape models-their training and application. Comput. Vis. Image Understand. 1995, 61:38-59.
13. Davatzikos C., Tao X., Shen D. Hierarchical active shape models, using the wavelet transform. IEEE TMI 2003, 22:414-423.
14. Davies R.H., Twining C.J., Cootes T.F., Waterton J.C., Taylor C.J. 3D statistical shape models using direct optimisation of description length. ECCV 2002, 3-20. Springer.
15. Essafi, S., Langs, G., Paragios, N., 2009. Hierarchical 3D diffusion wavelet shape priors. In: IEEE CVPR, pp. 1717-1724.
16. Huang X., Dione D.P., Compas C.B., Papademetris X., Lin B.A., Bregasi A., Sinusas A.J., Staib L.H., Duncan J.S. Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Med. Image Anal. 2014, 18:253-271.
17. Kaiser H.F. The varimax criterion for analytic rotation in factor analysis. Psychometrika 1958, 23:187-200.
18. Kass M., Witkin A., Terzopoulos D. Snakes: active contour models. IJCV 1988, 1:321-331.
19. Lankton, S., 2009. Sparse field methods-technical report. Georgia Institute of Technology.
20. Lankton S., Tannenbaum A. Localizing region-based active contours. IEEE TIP 2008, 17:2029-2039.
21. Li, Y., Tan, T., Volkau, I., Nowinski, W.L., 2007. Model-guided segmentation of 3D neuroradiological image using statistical surface wavelet model. In: IEEE CVPR, pp. 1-7.
22. Mitchell S.C., Bosch J.G., Lelieveldt B.P., van der Geest R.J., Reiber J.H., Sonka M. 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE TMI 2002, 21:1167-1178.
23. Nain D., Haker S., Bobick A., Tannenbaum A.R. Multiscale 3D shape analysis using spherical wavelets. MICCAI 2005, 459-467. Springer.
24. Nain D., Haker S., Bobick A., Tannenbaum A. Shape-driven 3D segmentation using spherical wavelets. MICCAI 2006, 66-74. Springer.
25. Noble J.A., Boukerroui D. Ultrasound image segmentation: a survey. IEEE TMI 2006, 25:987-1010.
26. Orderud, F., 2006. A framework for real-time left ventricular tracking in 3D+T echocardiography, using nonlinear deformable contours and Kalman filter based tracking. In: IEEE Computers in Cardiology, pp. 125-128.
27. Orderud F., Hansgård J., Rabben S.I. Real-time tracking of the left ventricle in 3D echocardiography using a state estimation approach. MICCAI 2007, 858-865. Springer.
28. Osher S., Sethian J.A. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 1988, 79:12-49.
29. Petitjean C., Dacher J. A review of segmentation methods in short axis cardiac MR images. Med. Image Anal. 2011, 15:169-184.
30. Piegl L., Tiller W. Curve and Surface Basics 1995, Springer.
31. Rajpoot, K., Grau, V., Noble, J.A., 2009. Local-phase based 3D boundary detection using monogenic signal and its application to real-time 3-D echocardiography images. In: IEEE ISBI, pp. 783-786.
32. Rajpoot K., Grau V., Noble J.A., Becher H., Szmigielski C. The evaluation of single-view and multi-view fusion 3d echocardiography using image-driven segmentation and tracking. Med. Image Anal. 2011, 15:514-528.
33. Rissanen J. A universal prior for integers and estimation by minimum description length. Annals Stat. 1983, 416-431.
34. Sethian J.A. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science 1999, vol. 3. Cambridge University Press.
35. Stam, J., 1998. Evaluation of loop subdivision surfaces.
36. Stegmann, M.B., Sjöstrand, K., Larsen, R., 2006. Sparse modeling of landmark and texture variability using the orthomax criterion. In: Medical Imaging, International Society for Optics and Photonics.
37. Taylor, J., Stebbing, R.V., Ramakrishna, V., Keskin, C., Shotton, J., Izadi, S., Hertzmann, A., Fitzgibbon, A.W., 2014. User-specific hand modeling from monocular depth sequences. In: IEEE CVPR.
38. Triggs B., McLauchlan P.F., Hartley R.I., Fitzgibbon A.W. Bundle adjustment-a modern synthesis. Vision Algorithms: Theory and Practice 2000, 298-372. Springer.
39. Wang, Y., Peterson, B.S., Staib, L.H., 2000. Shape-based 3D surface correspondence using geodesics and local geometry. In: IEEE CVPR, pp. 644-651.
40. Whitaker R.T. A level-set approach to 3D reconstruction from range data. IJCV 1998, 29:203-231.
41. Zhou, X., Huang, X., Duncan, J.S., Yu, W., 2013. Active contours with group similarity. In: IEEE CVPR, pp. 2969-2976.