Abstract
Statistical shape modeling (SSM) is a widely popular framework in cardiac image analysis, especially for image segmentation and computer-aided diagnosis. However, the conventional PCA-based models produce new axes of variation which are statistically motivated but thus are not necessarily clinically meaningful. In this paper, we propose an alternative method for statistical decomposition of the shape variability based on partial least squares (PLS). With this method, the model construction is achieved such that it is constrained by the specific clinical question of interest (e.g., estimation of disease state). To achieve this, instead of deriving modes of variation in the directions of maximal variation as in PCA, PLS searches for new axes of variation that correlate most with some output clinical response variables such as diagnostic labels, leading to a decomposition that is anatomically and clinically more meaningful. The validation carried out with 200 cases from the Cardiac Atlas Project database as part of the MICCAI 2015 challenge on SSM, including healthy and infarcted left ventricles, shows the strength of the proposed PLS-based statistical shape model, with 98 % prediction accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hoogendoorn, C., Duchateau, N., Sánchez-Quintana, D., Whitmarsh, T., Sukno, F., De Craene, M., Lekadir, K., Frangi, A.: A high-resolution atlas and statistical model of the human heart from multislice CT. IEEE Trans. Med. Imaging 32(1), 28–44 (2013)
Hoogendoorn, C., Pashaei, A., Sebastian, R., Sukno, F.M., Cámara, O., Frangi, A.F.: Sensitivity analysis of mesh warping and subsampling strategies for generating large scale electrophysiological simulation data. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 418–426. Springer, Heidelberg (2011)
Bosch, J.G., Nijland, F., Mitchell, S.C., Lelieveldt, B.P.F., Kamp, O., Sonka, M., Reiber, J.H.C.: Computer-aided diagnosis via model-based shape analysis: automated classification of wall motion abnormali-ties in echocardiograms. Acad. Radiol. 12(3), 358–367 (2005)
Zhao, F., Zhang, H., Wahle, A., Stolpen, A., Scholz, T., Sonka, M.: Congenital aortic disease: 4D magnetic resonance segmentation and quantitative analysis. Med. Image Anal. 13(3), 483–493 (2009)
Sjoestrand, K., Stegmann, M.B., Larsen, R.: Sparse principal component analysis in medical shape modeling. In: SPIE Medical Imaging: Image Processing (2006)
Leung, K., Bosch, J.G.: Localized shape variations for classifying wall motion in echocardiograms. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 52–59. Springer, Heidelberg (2007)
Lekadir, K., Keenan, N., Pennell, D., Yang, G.Z.: Shape-based myocardial contractility analysis using multivariate outlier detection. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 834–841. Springer, Heidelberg (2007)
Lekadir, K., Keenan, N., Pennell, D., Yang, G.-Z.: An inter-landmark approach to 4-D shape extraction and interpretation: Application to myocardial motion assessment in MRI. IEEE Trans. Med. Imaging 30(1), 52–68 (2011)
Suinesiaputra, A., Frangi, A.F., Kaandorp, T., Lamb, H.J., Bax, J.J., Reiber, J., Lelieveldt, B.: Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images. IEEE Trans. Med. Imaging 28(4), 595–607 (2009)
Wold, S., Geladi, P., Esbensen, K., Öhman, J.: Multi-way principal components-and PLS-analysis. J. Chemometr. 1(1), 41–56 (1987)
Rao, A., Aljabar, P., Rueckert, D.: Hierarchical statistical shape analysis and prediction of sub-cortical brain structures. Med. Image Anal. 12(1), 55–68 (2008)
Lekadir, K., Hoogendoorn, C., Hazrati-Marangalou, J., Taylor, Z., Noble, C., Van Rietbergen, B., Frangi, A.: A predictive model of vertebral trabecular anisotropy from ex vivo micro-CT. IEEE Trans. Med. Imaging 34, 1747–1759 (2015)
Lekadir, K., Pashaei, A., Hoogendoorn, C., Pereanez, M., Alba, X., Frangi, A.F.: Effect of statistically derived fiber models on the estimation of cardiac electrical activation. IEEE Trans. Biomed. Eng. 61(11), 2740–2748 (2014)
Lekadir, K., Hazrati-Marangalou, J., Hoogendoorn, C., Taylor, Z., van Rietbergen, B., Frangi, A.F.: Statistical estimation of femur micro-architecture using optimal shape and density predictors. J. Biomech. 48(4), 598–603 (2015)
Lekadir, K., Hoogendoorn, C., Pereanez, M., Alba, X., Pashaei, A., Frangi, A.F.: Statistical personalization of ventricular fiber orientation using shape predictors. IEEE Trans. Med. Imaging 33(4), 882–890 (2014)
McIntosh, A.R., Lobaugh, N.J.: Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage 23, S250–S263 (2004)
Abdi, H.: Partial least squares regression (PLS-regression), Thousand Oaks, CA, Sage, pp. 792–795 (2003)
Pérez-Enciso, M., Tenenhaus, M.: Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Hum. Genet. 112(5–6), 581–592 (2003)
Chevallier, S., Bertrand, D., Kohler, A., Courcoux, P.: Application of PLS-DA in multivariate image analysis. J. Chemometr. 20(5), 221 (2006)
Fonseca, C.G., Backhaus, M., Bluemke, D.A., Britten, R.D., Chung, J.D., Cowan, B.R., Dinov, I.D., Finn, J.P., et al.: The cardiac atlas project–an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Lekadir, K., Albà, X., Pereañez, M., Frangi, A.F. (2016). Statistical Shape Modeling Using Partial Least Squares: Application to the Assessment of Myocardial Infarction. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-28712-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28711-9
Online ISBN: 978-3-319-28712-6
eBook Packages: Computer ScienceComputer Science (R0)