Deep Generative Networks for Heterogeneous Augmentation of Cranial
  Defects
Kamil Kwarciak Marek Wodzinski
The design of personalized cranial implants is a challenging and tremendous
task that has become a hot topic in terms of process automation with the use of
deep learning techniques. The main challenge is associated with the high
diversity of possible cranial defects. The lack of appropriate data sources
negatively influences the data-driven nature of deep learning algorithms.
Hence, one of the possible solutions to overcome this problem is to rely on
synthetic data. In this work, we propose three volumetric variations of deep
generative models to augment the dataset by generating synthetic skulls, i.e.
Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP),
WGAN-GP hybrid with Variational Autoencoder pretraining (VAE/WGAN-GP) and
Introspective Variational Autoencoder (IntroVAE). We show that it is possible
to generate dozens of thousands of defective skulls with compatible defects
that achieve a trade-off between defect heterogeneity and the realistic shape
of the skull. We evaluate obtained synthetic data quantitatively by defect
segmentation with the use of V-Net and qualitatively by their latent space
exploration. We show that the synthetically generated skulls highly improve the
segmentation process compared to using only the original unaugmented data. The
generated skulls may improve the automatic design of personalized cranial
implants for real medical cases.