  Thousands of people suffer from cranial injuries every year. They require
personalized implants that need to be designed and manufactured before the
reconstruction surgery. The manual design is expensive and time-consuming
leading to searching for algorithms whose goal is to automatize the process.
The problem can be formulated as volumetric shape completion and solved by deep
neural networks dedicated to supervised image segmentation. However, such an
approach requires annotating the ground-truth defects which is costly and
time-consuming. Usually, the process is replaced with synthetic defect
generation. However, even the synthetic ground-truth generation is
time-consuming and limits the data heterogeneity, thus the deep models'
generalizability. In our work, we propose an alternative and simple approach to
use a self-supervised masked autoencoder to solve the problem. This approach by
design increases the heterogeneity of the training set and can be seen as a
form of data augmentation. We compare the proposed method with several
state-of-the-art deep neural networks and show both the quantitative and
qualitative improvement on the SkullBreak and SkullFix datasets. The proposed
method can be used to efficiently reconstruct the cranial defects in real time.
