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.

翻译：个性化颅骨植入物的设计是一项极具挑战性的繁重任务，随着深度学习技术在流程自动化中的应用，该领域已成为研究热点。主要挑战在于颅骨缺损的高度多样性，而数据源的匮乏对深度学习算法的数据驱动特性产生负面影响。因此，克服这一问题的可行方案之一在于依赖合成数据。本研究提出了三种基于体积的深度生成模型变体，通过生成合成颅骨来扩充数据集，具体包括：梯度惩罚Wasserstein生成对抗网络（WGAN-GP）、结合变分自编码器预训练的混合WGAN-GP模型（VAE/WGAN-GP）以及内省变分自编码器（IntroVAE）。实验表明，该方法能够生成数万个具有兼容性缺损的颅骨模型，在缺损异质性与颅骨真实形态之间取得平衡。我们通过V-Net进行缺损分割对合成数据进行定量评估，并通过潜在空间探索进行定性分析。研究结果显示，与仅使用原始未扩充数据相比，合成颅骨可显著提升分割性能。这些生成的颅骨模型有望改善真实临床病例中个性化颅骨植入物的自动化设计。