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.

模拟和制造个性化颅骨植入物是可以缩短患有颅骨损伤患者等待时间的重要研究领域。个性化植入物的建模可以通过深度学习方法部分自动化，然而，该任务在使用以前未见过的数据分布时难以泛化，难以在实际临床环境中使用研究成果。鉴于获取地面真实注释的困难，必须考虑和引入不同的技术来改善用于训练深度网络的数据集的异质性。在这项工作中，我们对几种数据增强技术进行了大规模研究，包括经典的几何转换、图像配准、变分自动编码器、生成对抗网络以及最新的潜在扩散模型。我们表明，大量数据增强的使用显著增加了定量和定性结果，使得 SkullBreak 数据集的平均 Dice 分数超过 0.94，SkullFix 数据集的平均 Dice 分数超过 0.96。此外，我们展示了合成增强网络成功重建真实临床缺陷。这项工作对于人工智能在个性化颅骨植入物的自动建模领域做出了重要贡献。

通过大量数据增强改进基于深度学习的颅骨缺损自动重建：从图像配准到潜在扩散模型

Improving Deep Learning-based Automatic Cranial Defect Reconstruction by  Heavy Data Augmentation: From Image Registration to Latent Diffusion Models

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.

利用深度学习技术生成合成颅骨数据以改善自动设计个性化颅骨植入物的过程。