Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks

We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-specific cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head.