Abstract
Cardiac shape and deformation are two relevant descriptors for the characterization of cardiovascular diseases. It is also known that strong interactions exist between them depending on the disease. In clinical routine, these high dimensional descriptors are reduced to scalar values (ventricular ejection fraction, volumes, global strains...), leading to a substantial loss of information. Methods exist to better integrate these high-dimensional data by reducing the dimension and mixing heterogeneous descriptors. Nevertheless, they usually do not consider the interactions between the descriptors. In this paper, we propose to apply dimensionality reduction on high dimensional cardiac shape and deformation descriptors and take into account their interactions. We investigated two unsupervised linear approaches, an individual analysis of each feature (Principal Component Analysis), and a joint analysis of both features (Partial Least Squares) and related their output to the main characteristics of the studied pathology. We experimented both methods on right ventricular meshes from a population of 254 cases tracked along the cycle (154 with pulmonary hypertension, 100 controls). Despite similarities in the output space obtained by the two methods, substantial differences are observed in the reconstructed shape and deformation patterns along the principal modes of variation, in particular in regions of interest for the studied disease.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, X., Cowan, B.R., Bluemke, D.A., et al.: Atlas-based quantification of cardiac remodeling due to myocardial infarction. PLoS ONE 9, e110243 (2014)
Bai, W., Shi, W., de Marvao, A., et al.: A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion. Med. Image Anal. 26, 133–145 (2015)
McLeod, K., Sermesant, M., Beerbaum, P., et al.: Spatio-temporal tensor decomposition of a polyaffine motion model for a better analysis of pathological left ventricular dynamics. IEEE Trans. Med. Imaging 34, 1562–1575 (2015)
Duchateau, N., De Craene, M., Piella, G., et al.: Constrained manifold learning for the characterization of pathological deviations from normality. Med. Image Anal. 16, 1532–1549 (2012)
Sanchez-Martinez, S., Duchateau, N., Erdei, T., et al.: Characterization of myocardial motion patterns by unsupervised multiple kernel learning. Med. Image Anal. 35, 70–82 (2017)
Bello, G.A., Dawes, T.J.W., Duan, J., et al.: Deep-learning cardiac motion analysis for human survival prediction. Nat. Mach. Intell. 1, 95–104 (2019)
Sanz, J., Sánchez-Quintana, D., Bossone, E., et al.: Anatomy, function, and dysfunction of the right ventricle: JACC state-of-the-art review. J. Am. Coll. Cardiol. 73, 1463–1482 (2019)
Valencia-Aguirre, J., Álvarez Meza, A., Daza-Santacoloma, G., Acosta-Medina, C., Castellanos-Domínguez, C.G.: Multiple manifold learning by nonlinear dimensionality reduction. In: San Martin, C., Kim, S.W. (eds.) CIARP 2011. LNCS, vol. 7042, pp. 206–213. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25085-9_24
Lee, C.S., Elgammal, A., Torki, M.: Learning representations from multiple manifolds. Pattern Recogn. 50, 74–87 (2016)
Benkarim, O.M., et al.: Revealing regional associations of cortical folding alterations with in utero ventricular dilation using joint spectral embedding. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 620–627. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_71
Ham, J., Lee, D.D., Saul, L.K., et al.: Semisupervised alignment of manifolds. In: Proceedings of the AISTATS, vol. 10 (2005)
Xiong, L., Wang, F., Zhang, C.: Semi-definite manifold alignment. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS, vol. 4701, pp. 773–781. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74958-5_79
Puyol-Antón, E., Sinclair, M., Gerber, B., et al.: A multimodal spatiotemporal cardiac motion atlas from MR and ultrasound data. Med. Image Anal. 40, 96–110 (2017)
Moceri, P., Duchateau, N., Baudouy, D., et al.: Three-dimensional right-ventricular regional deformation and survival in pulmonary hypertension. Eur. Heart J. Cardiovasc. Imaging 19, 450–458 (2018)
López-Candales, A., Rajagopalan, N., Gulyasy, B., et al.: Differential strain and velocity generation along the right ventricular free wall in pulmonary hypertension. Can. J. Cardiol. 25, 73–77 (2009)
Seo, H.S., Lee, H.: Assessment of right ventricular function in pulmonary hypertension with multimodality imaging. J. Cardiovasc. Imaging 26, 189 (2018)
Gower, J.C.: Generalized Procrustes analysis. Psychometrika 40, 33–51 (1975)
Wegelin, A.: A survey of partial least squares (PLS) methods, with emphasis on the two-block case (2000)
Triposkiadis, F., Butler, J., Abboud, F.M., et al.: The continuous heart failure spectrum: moving beyond an ejection fraction classification. Eur. Heart J. 40, 2155–2163 (2019)
Kind, T., Mauritz, G.-J., Marcus, J.T., et al.: Right ventricular ejection fraction is better reflected by transverse rather than longitudinal wall motion in pulmonary hypertension. J. Cardiovasc. Magn. Reson. 12, 35 (2010)
Schreckenberg, M.: Adaptation of a 3D-surface model to boundaries of an anatomical structure in a 3D-image data set. US Patent, US9280816B2 (2013)
Guigui, N., Jia, S., Sermesant, M., Pennec, X.: Symmetric algorithmic components for shape analysis with diffeomorphisms. In: Nielsen, F., Barbaresco, F. (eds.) GSI 2019. LNCS, vol. 11712, pp. 759–768. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26980-7_79
Acknowledgements
The authors acknowledge the partial support from the French ANR (LABEX PRIMES of Université de Lyon [ANR-11-LABX-0063], within the program Investissements d’Avenir [ANR-11-IDEX-0007]), and from the EEA doctoral school.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Di Folco, M., Clarysse, P., Moceri, P., Duchateau, N. (2020). Learning Interactions Between Cardiac Shape and Deformation: Application to Pulmonary Hypertension. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. STACOM 2019. Lecture Notes in Computer Science(), vol 12009. Springer, Cham. https://doi.org/10.1007/978-3-030-39074-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-39074-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39073-0
Online ISBN: 978-3-030-39074-7
eBook Packages: Computer ScienceComputer Science (R0)