  Modeling and manufacturing of personalized cranial implants are important
research areas that may decrease the waiting time for patients suffering from
cranial damage. The modeling of personalized implants may be partially
automated by the use of deep learning-based methods. However, this task suffers
from difficulties with generalizability into data from previously unseen
distributions that make it difficult to use the research outcomes in real
clinical settings. Due to difficulties with acquiring ground-truth annotations,
different techniques to improve the heterogeneity of datasets used for training
the deep networks have to be considered and introduced. In this work, we
present a large-scale study of several augmentation techniques, varying from
classical geometric transformations, image registration, variational
autoencoders, and generative adversarial networks, to the most recent advances
in latent diffusion models. We show that the use of heavy data augmentation
significantly increases both the quantitative and qualitative outcomes,
resulting in an average Dice Score above 0.94 for the SkullBreak and above 0.96
for the SkullFix datasets. Moreover, we show that the synthetically augmented
network successfully reconstructs real clinical defects. The work is a
considerable contribution to the field of artificial intelligence in the
automatic modeling of personalized cranial implants.
