Statistical shape analysis plays a key role in various medical imaging applications. In particular , such methods provide tools for registering, deforming, comparing, averaging, and modeling anatomical shapes. In this work, we focus on the application of a recent method for statistical shape analysis of elastic parametrized surfaces to simulation of realistic en-dometrial tissue shapes. The clinical data used here contains ten magnetic resonance imaging (MRI) endometrial tissue surfaces, which are used to learn a generative shape model. We generate random samples from this model, and apply elastic semi-synthetic deformations to the randomly generated tissue shapes. This provides two types of simulated data: (1) MRI-type (without deformation) and (2) corresponding transvaginal utltrasound (TVUS) type endometrial tissue shapes, which undergo a deformation due to the transducer's pressure. The proposed models can be used for validation purposes of automatic, multimodal image registration techniques, which are crucial steps in diagnosing endometriosis.