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The automatic design of cranial implants is an important and challenging task. The implants must be designed according to the individual characterization of the patient’s defect. This makes the process tedious and time consuming. However, if possible, the personalized implants should be designed and fabricated during the surgical procedure that requires the implant modeling to be as efficient as possible.

The design of the cranial implants may be improved and accelerated by deep learning-based segmentation networks. This approach transfers the computational burden to the training phase, allowing a real-time inference. Moreover, the practical method should be fully automatic, without the need for manual parameter tuning related to the defect characterization. Therefore, a single, universal model is desirable during practical usage. Nevertheless, deep learning-based solutions require large …