During the past ten years, the biophysical modelling of the human body has been a topic of increasing interest in the field of biomedical image analysis. The aim of such modelling is to formulate personalized medicine where a digital model of an organ can be adjusted to a patient from clinical data. This virtual organ would enable to estimate the parameters which are difficult to quantify in clinical routine, such as pressure, and to test computer-based therapies that can predict the evolution of the organ over time and with therapy. Nevertheless, in order to be able to translate such an approach to clinical practice, there is a crucial demand for robust statistical methods for studying multiple cases in a patient population, in order to be able to understand the effect of different clinical factors on the anatomy and extract the significant phenomena. Such statistical analyses can both provide a predictive model and guide the biophysical approach. However, computing statistics on such complex objects (i.e. 3D shapes) is very challenging. It was traditionally relying on point based discretisation of the shapes where the point-to-point correspondence is an important limiting factor for the usability of the method. New approaches were recently developed to compute such statistics without this limitation [1], and we present in this paper an application of these along with an open source tool made available through the VPH Network of Excellence Toolkit that allows multiple patients to be compared and analysed using this statistical method. The tools can be downloaded from http://www-sop.inria.fr/asclepios/projects/Health-e-Child/ShapeAnalysis/index.php.