  Stochastically evolving geometric systems are studied in shape analysis and
computational anatomy for modelling random evolutions of human organ shapes.
The notion of geodesic paths between shapes is central to shape analysis and
has a natural generalisation as diffusion bridges in a stochastic setting.
Simulation of such bridges is key to solve inference and registration problems
in shape analysis. We demonstrate how to apply state-of-the-art diffusion
bridge simulation methods to recently introduced stochastic shape deformation
models thereby substantially expanding the applicability of such models. We
exemplify these methods by estimating template shapes from observed shape
configurations while simultaneously learning model parameters.
