Abstract
This chapter provides a detailed framework for anatomical shape parametrization for reduced order modeling of living tissues and organs biophysics. In particular, the parameterization of the anatomy is obtained using statistical shape analysis on data sets of medical images, thus offering a fundamental tool for patient-specific modeling in reduced-order complexity. The goal of parametric shape models is twofold: extracting a low dimensional set of shape features describing the variability of organs anatomies from image collections and creating generative models capable of producing anatomically consistent shapes for an arbitrary choice of the shape parameters for the reliable training of reduced-order models
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