  Cranial implant design is a challenging task, whose accuracy is crucial in
the context of cranioplasty procedures. This task is usually performed manually
by experts using computer-assisted design software. In this work, we propose
and evaluate alternative automatic deep learning models for cranial implant
reconstruction from CT images. The models are trained and evaluated using the
database released by the AutoImplant challenge, and compared to a baseline
implemented by the organizers. We employ a simulated virtual craniectomy to
train our models using complete skulls, and compare two different approaches
trained with this procedure. The first one is a direct estimation method based
on the UNet architecture. The second method incorporates shape priors to
increase the robustness when dealing with out-of-distribution implant shapes.
Our direct estimation method outperforms the baselines provided by the
organizers, while the model with shape priors shows superior performance when
dealing with out-of-distribution cases. Overall, our methods show promising
results in the difficult task of cranial implant design.
