Shape parametrization of bio-mechanical finite element models based on medical images

Abstract : The main objective of this study is to combine the statistical shape analysis (SSA) with a morphing procedure in order to generate shape-parametric finite element models of tissues and organs and to explore the reliability and the limitations of this approach when applied to databases of real medical images. As classical statistical shape models are not always adapted to the morphing procedure, a new registration method was developed in order to maximize the morphing efficiency. The method was compared to the traditional iterative thin plate spline (iTPS). Two data sets of 33 proximal femora shapes and 385 liver shapes were used for the comparison. The principal component analysis (PCA) was used to get the principal morphing modes. In terms of anatomical shape reconstruction (evaluated through the criteria of generalization, compact-ness and specificity), our approach compared fairly well to the iTPS method, while performing remarkably better in terms of mesh quality, since it was less prone to generate invalid meshes in the interior. This was particularly true in the liver case. Such methodology offers a potential application for the generation of automated finite element (FE) models from medical images. Parametrized anatomical models can also be used to assess the influence of inter-patient variability on the biomechanical response of the tissues. Indeed, thanks to the shape parametrization the user would easily have access to a valid FE model for any shape belonging to the parameters subspace.
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Contributeur : Nathan Lauzeral <>
Soumis le : jeudi 28 mars 2019 - 11:38:54
Dernière modification le : mercredi 12 juin 2019 - 17:01:58
Archivage à long terme le : samedi 29 juin 2019 - 13:58:03


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Nathan Lauzeral, Domenico Borzacchiello, Michael Kugler, Daniel George, Yves Rémond, et al.. Shape parametrization of bio-mechanical finite element models based on medical images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Taylor & Francis, inPress, ⟨10.1080/21681163.2018.1447400⟩. ⟨hal-01925458v2⟩



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