Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Statistical Shape Modeling and Segmentation of the Left Ventricle Endocardium from CMR Images based on Different Anatomical Landmark Alignments

Document Type : Full Research Paper

Authors

Gelareh Valizadeh 1
Farshid Babapour Mofrad 2
Ahmad Shalbaf 3

1 Ph.D. Student, Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Assistant Professor, Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Assistant Professor, Department of Biomedical Engineering & Medical Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran

https://doi.org/10.22041/ijbme.2020.130962.1607
Abstract
Statistical Shape Modeling is widely used in many applications of cardiac images. Many efforts have been done to generate optimized Statistical Shape Models (SSMs). In this paper, we evaluated three different 3D endocardial models constructed using different alignment procedures. From 20 healthy CMR datasets, three different endocardial models are generated by varying the surface alignment methods means based on the Center of the Apex (CoA), the Center of Mass (CoM), and the Center of the Basal (CoB) of the endocardium. Then Principle Component Analysis (PCA) is applied to show the maximum variation of the SSMs. The constructed statistical models are compared by measuring the compactness, generalization ability, and specificity. Besides, the performance of each model in the 3D endocardial segmentation application using the Active Shape Model (ASM) technique is evaluated by the Hausdorff Distance (HD) criterion. The results indicate that the CoB-based model is less compact than the CoA-based model but more compact than the CoM-based model. Although for a constant number of modes the reconstruction error is approximately the same for all models, surface alignment based on CoB leads to generate a more specific model than the others. The resulted HDs show that the CoB alignment strategy produces the ASM which has the best performance in 3D endocardial segmentation among the other models. The computed results from the quantitative analysis demonstrate that varying alignment strategies affect the quality of the constructed SSM. It is obvious that the specificity and segmentation accuracy of the proposed CoB-based model outperforms the classical CoM-based approach.

Keywords

Statistical Shape Modeling
Left Ventricle
Active Shape Model
Endocardium
Segmentation
Cardiac MRI
Model Evaluation

Subjects

  1. Shalbaf, H. Behnam, Z. Alizade-Sani, and M. Shojaifard, "Automatic classification of left ventricular regional wall motion abnormalities in echocardiography images using nonrigid image registration," Journal of digital imaging, vol. 26, no. 5, pp. 909-919, 2013.
  2. Alizadeh Sani, A. Shalbaf, H. Behnam, and R. Shalbaf, "Automatic Computation of Left Ventricular Volume Changes Over a Cardiac Cycle from Echocardiography Images by Nonlinear Dimensionality Reduction," Journal of Digital Imaging, vol. 28, no. 1, 2015.
  3. Masaeli, H. Behnam, Z. Alizadeh Sani, and A. Shalbaf, "Tracking of Cardiac Wall Motion using Nonrigid Registration of Echocardiographic Images," Iranian Journal of Biomedical Engineering, vol. 7, no. 3, pp. 237-254, 2013, doi: 10.22041/ijbme.2013.13207.
  4. Piazzese et al., "Segmentation of the left ventricular endocardium from magnetic resonance images by using different statistical shape models," Journal of electrocardiology, vol. 49, no. 3, pp. 383-391, 2016.
  5. Bogaert, S. Dymarkowski, A. M. Taylor, and V. Muthurangu, Clinical cardiac MRI. Springer Science & Business Media, 2012.
  6. Suinesiaputra et al., "Statistical shape modeling of the left ventricle: myocardial infarct classification challenge," IEEE journal of biomedical and health informatics, vol. 22, no. 2, pp. 503-515, 2017.
  7. Biffi, J. L. Bruse, M. A. Zuluaga, H. N. Ntsinjana, A. M. Taylor, and S. Schievano, "investigating cardiac Motion Patterns Using synthetic high-resolution 3D cardiovascular Magnetic resonance images and statistical shape analysis," Frontiers in pediatrics, vol. 5, p. 34, 2017.
  8. L. Bruse et al., "CMR-based 3D statistical shape modelling reveals left ventricular morphological differences between healthy controls and arterial switch operation survivors," Journal of Cardiovascular Magnetic Resonance, vol. 18, no. S1, p. Q2, 2016.
  9. De Craene et al., "SPM to the heart: mapping of 4D continuous velocities for motion abnormality quantification," in 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), 2012: IEEE, pp. 454-457.
  10. Perperidis, R. Mohiaddin, and D. Rueckert, "Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2005: Springer, pp. 402-410.
  11. Babapour Mofrad, A. Abbaspour Tehrani-Fard, R. Aghaeizadeh Zoroofi, S. Akhlaghpoor, and Y.-W. Chen, "A novel wavelet based multi-scale statistical shape model-analysis for the liver application: segmentation and classification," Current Medical Imaging, vol. 6, no. 3, pp. 145-155, 2010.
  12. Afzali, F. B. Mofrad, and M. Pouladian, "Contour-based lung shape analysis in order to tuberculosis detection: modeling and feature description," Medical & Biological Engineering & Computing, pp. 1-22, 2020.
  13. Heimann and H.-P. Meinzer, "Statistical shape models for 3D medical image segmentation: a review," Medical image analysis, vol. 13, no. 4, pp. 543-563, 2009.
  14. A. Amirkhizi and S. Haghipour, "Left Ventricle Segmentation in Cardiac Magnetic Resonance Imaging (MRI) by Modified Active Contour Method," Majallah-i pizishki-i Danishgah-i Ulum-i Pizishki va Khadamat-i Bihdashti-i Darmani-i Tabriz, vol. 35, no. 6, p. 12, 2014.
  15. Zhu, X. Papademetris, A. Sinusas, and J. S. Duncan, "Segmentation of left ventricle from 3D cardiac MR image sequences using a subject-specific dynamical model," in 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008: IEEE, pp. 1-8.
  16. Lu, P. Radau, K. Connelly, A. Dick, and G. A. Wright, "Segmentation of left ventricle in cardiac cine MRI: An automatic image-driven method," in International Conference on Functional Imaging and Modeling of the Heart, 2009: Springer, pp. 339-347.
  17. F. Frangi, D. Rueckert, J. A. Schnabel, and W. J. Niessen, "Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling," IEEE transactions on medical imaging, vol. 21, no. 9, pp. 1151-1166, 2002.
  18. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Active shape models-their training and application," Computer vision and image understanding, vol. 61, no. 1, pp. 38-59, 1995.
  19. C. van Assen, R. J. van der Geest, M. G. Danilouchkine, H. J. Lamb, J. H. Reiber, and B. P. Lelieveldt, "Three-dimensional active shape model matching for left ventricle segmentation in cardiac CT," in Medical Imaging 2003: Image Processing, 2003, vol. 5032: International Society for Optics and Photonics, pp. 384-393.
  20. Lorenzo-Valdés, G. I. Sanchez-Ortiz, A. G. Elkington, R. H. Mohiaddin, and D. Rueckert, "Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm," Medical Image Analysis, vol. 8, no. 3, pp. 255-265, 2004.
  21. Pilgram et al., "Shape discrimination of healthy and diseased cardiac ventricles using medial representation," International Journal of Computer Assisted Radiology and Surgery, vol. 1, no. 1, pp. 33-38, 2006.
  22. G. Caiani et al., "Three-dimensional left ventricular segmentation from magnetic resonance imaging for patient-specific modelling purposes," Europace, vol. 16, no. suppl_4, pp. iv96-iv101, 2014.
  23. Afzali, F. B. Mofrad, and M. Pouladian, "2D Statistical Shape Model for Lung Using Apex Anatomical Landmark-based Registration Criteria," in 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 2019: IEEE, pp. 21-25.
  24. Bernard et al., "Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?," IEEE transactions on medical imaging, vol. 37, no. 11, pp. 2514-2525, 2018.
  25. Andreopoulos and J. K. Tsotsos, "Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI," Medical Image Analysis, vol. 12, no. 3, pp. 335-357, 2008.
  26. Valizadeh, F. B. Mofrad, and A. Shalbaf, "Impacts of Spherical Harmonics Shape Descriptors on the Inter-Slice Interpolation of MR Images," in 2019 26th National and 4th International Iranian Conference on Biomedical Engineering (ICBME), 2019: IEEE, pp. 26-30.
  27. T. Herman, J. Zheng, and C. A. Bucholtz, "Shape-based interpolation," IEEE Computer Graphics and Applications, no. 3, pp. 69-79, 1992.
  28. P. Raya and J. K. Udupa, "Shape-based interpolation of multidimensional objects," IEEE transactions on medical imaging, vol. 9, no. 1, pp. 32-42, 1990.
  29. E. Lorensen and H. E. Cline, "Marching cubes: A high resolution 3D surface construction algorithm," ACM siggraph computer graphics, vol. 21, no. 4, pp. 163-169, 1987.
  30. Franc and V. Skala, "Fast algorithm for triangular mesh simplification based on vertex decimation," in International Conference on Computational Science, 2002: Springer, pp. 42-51.
  31. Knapp, "Mesh decimation using VTK," Institute of Computer Graphics and Algorithms, Vienna University of Technology, 2002.
  32. Kobbelt, S. Campagna, and H.-P. Seidel, "A general framework for mesh decimation," in Graphics interface, 1998, vol. 98, pp. 43-50.
  33. Fang and D. A. Boas, "Tetrahedral mesh generation from volumetric binary and grayscale images," in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009: Ieee, pp. 1142-1145.
  34. Delavari, A. H. Foruzan, and B. V. Chen, "Improvement of Statistical Shape Models for Non-rigid Tissues Using Coherent Point Drift Algorithm," Iranian Journal of Biomedical Engineering, vol. 8, no. 3, pp. 213-227, 2014, doi: 10.22041/ijbme.2014.13287.
  35. Myronenko and X. Song, "Point set registration: Coherent point drift," IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 12, pp. 2262-2275, 2010.
  36. C. Gower, "Generalized procrustes analysis," Psychometrika, vol. 40, no. 1, pp. 33-51, 1975.
  37. Wold, K. Esbensen, and P. Geladi, "Principal component analysis," Chemometrics and intelligent laboratory systems, vol. 2, no. 1-3, pp. 37-52, 1987.
  38. A. Styner et al., "Evaluation of 3D correspondence methods for model building," in Biennial International Conference on Information Processing in Medical Imaging, 2003: Springer, pp. 63-75.
  39. H. Davies, Learning shape: optimal models for analysing natural variability. University of Manchester Manchester, 2002.
  40. P. Huttenlocher and K. Kedem, "Computing the minimum Hausdorff distance for point sets under translation," in Proceedings of the sixth annual symposium on Computational geometry, 1990, pp. 340-349.
  41. B. Mofrad, R. A. Zoroofi, Y.-W. Chen, A. A. Tehrani-Fard, Y. Sato, and A. Furukawa, "Evaluation of liver shape approximation and characterization," in 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009: IEEE, pp. 1297-1300.
  42. Kirschner, M. Becker, and S. Wesarg, "3D active shape model segmentation with nonlinear shape priors," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011: Springer, pp. 492-499.
  43. Lötjönen, S. Kivistö, J. Koikkalainen, D. Smutek, and K. Lauerma, "Statistical shape model of atria, ventricles and epicardium from short-and long-axis MR images," Medical image analysis, vol. 8, no. 3, pp. 371-386, 2004.
  44. Wang and C. Shi, "Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy," Biomedical engineering online, vol. 16, no. 1, p. 49, 2017.
  45. -J. Kroon, "Active shape model (ASM) and active appearance model (AAM)." [Online].Available:https://www.mathworks.com/matlabcentral/fileexchange/26706-active-shape-model-asm-and-active-appearance-model-aam.
  46. F. Cootes, D. H. Cooper, C. J. Taylor, and J. Graham, "Trainable method of parametric shape description," Image and Vision Computing, vol. 10, no. 5, pp. 289-294, 1992.
  47. Gerig, M. Jomier, and M. Chakos, "Valmet: A new validation tool for assessing and improving 3D object segmentation," in International conference on medical image computing and computer-assisted intervention, 2001: Springer, pp. 516-523.
  48. Cárdenes, R. de Luis-García, and M. Bach-Cuadra, "A multidimensional segmentation evaluation for medical image data," Computer methods and programs in biomedicine, vol. 96, no. 2, pp. 108-124, 2009.
  49. Gouttard, M. Styner, M. Prastawa, J. Piven, and G. Gerig, "Assessment of reliability of multi-site neuroimaging via traveling phantom study," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2008: Springer, pp. 263-270.
  50. Morain-Nicolier, S. Lebonvallet, E. Baudrier, and S. Ruan, "Hausdorff distance based 3D quantification of brain tumor evolution from MRI images," in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007: IEEE, pp. 5597-5600.
  51. Khotanlou, "3D brain tumors and internal brain structures segmentation in MR images," 2008.
  52. Olveres, E. Carbajal-Degante, B. Escalante-Ramírez, E. Vallejo, and C. M. García-Moreno, "Deformable models for segmentation based on local analysis," Mathematical Problems in Engineering, vol. 2017, 2017.
  53. F. Roohi and R. A. Zoroofi, "4D statistical shape modeling of the left ventricle in cardiac MR images," International journal of computer assisted radiology and surgery, vol. 8, no. 3, pp. 335-351, 2013.
  54. Davies, C. Twining, and C. Taylor, Statistical models of shape: Optimisation and evaluation. Springer Science & Business Media, 2008.
  55. Santiago, J. C. Nascimento, and J. S. Marques, "A 3D active shape model for left ventricle segmentation in MRI," Biomedical Image Segmentation: Advances and Trends, p. 99, 2016.
  56. O'Brien, O. Ghita, and P. F. Whelan, "Segmenting the left ventricle in 3D using a coupled ASM and a learned non-rigid spatial model," 2009.
  57. Santiago, J. C. Nascimento, and J. S. Marques, "A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumes," Neural Computing and Applications, vol. 28, no. 9, pp. 2489-2500, 2017.
  58. Butakoff et al., "Left-ventricular epi-and endocardium extraction from 3D ultrasound images using an automatically constructed 3D ASM," Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 4, no. 5, pp. 265-280, 2016.
  59. C. van Assen et al., "Cardiac LV segmentation using a 3D active shape model driven by fuzzy inference," in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2003: Springer, pp. 533-540.
  60. C. Van Assen et al., "SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data," Medical image analysis, vol. 10, no. 2, pp. 286-303, 2006.
  61. Peng, K. Lekadir, A. Gooya, L. Shao, S. E. Petersen, and A. F. Frangi, "A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging," Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 29, no. 2, pp. 155-195, 2016.
  62. C. Carminati et al., "A statistical shape model of the left ventricle from real-time 3D echocardiography and its application to myocardial segmentation of cardiac magnetic resonance images," Computers in biology and medicine, vol. 96, pp. 241-251, 2018.
  63. V. Romaguera, M. G. F. Costa, F. P. Romero, and C. F. F. Costa Filho, "Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks," in Medical Imaging 2017: Computer-Aided Diagnosis, 2017, vol. 10134: International Society for Optics and Photonics, p. 101342Z.
  64. Du et al., "Deep regression segmentation for cardiac bi-ventricle MR images," IEEE Access, vol. 6, pp. 3828-3838, 2018.
  65. Xia, M. Huo, Q. Wei, F. Liu, and S. Crozier, "Analysis of cardiac ventricular wall motion based on a three-dimensional electromechanical biventricular model," Physics in Medicine & Biology, vol. 50, no. 8, p. 1901, 2005.
Iranian Journal of Biomedical Engineering (IJBME)
Volume 14, Issue 4
Winter 2021
Pages 291-306

  • Receive Date 14 July 2020
  • Revise Date 15 October 2020
  • Accept Date 15 October 2020

Home

Contact Us