# Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects

arXiv: https://arxiv.org/abs/2308.04883 · HTML: https://mlsanity.com/paper/2308.04883/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Adversarially Learned Inference](https://mlsanity.com/paper/1606.00704/) (2016)
- [3D Human Pose Estimation in the Wild by Adversarial Learning](https://mlsanity.com/paper/1803.09722/) (2018)
- [IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis](https://mlsanity.com/paper/1807.06358/) (2018)
- [RAVE: A variational autoencoder for fast and high-quality neural audio synthesis](https://mlsanity.com/paper/2111.05011/) (2021)
- [Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks](https://mlsanity.com/paper/1908.02498/) (2019)
- [A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge](https://mlsanity.com/paper/2006.12449/) (2020)
- [Deep Learning-based Framework for Automatic Cranial Defect Reconstruction and Implant Modeling](https://mlsanity.com/paper/2204.06310/) (2022)
- [Text Generation Based on Generative Adversarial Nets with Latent Variable](https://mlsanity.com/paper/1712.00170/) (2017)
- [Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement](https://mlsanity.com/paper/2009.10769/) (2020)
- [Self-Supervised Object Detection via Generative Image Synthesis](https://mlsanity.com/paper/2110.09848/) (2021)

## Built on by (most cited first, 4 of 4)

- [Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders](https://mlsanity.com/paper/2404.13106/) (2024)
- [Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models](https://mlsanity.com/paper/2406.06372/) (2024)
- [MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching](https://mlsanity.com/paper/2606.24433/) (2026)
- [UniDCF: A Foundation Model for Comprehensive Dentocraniofacial Hard Tissue Reconstruction](https://mlsanity.com/paper/2508.11728/) (2025)

## Abstract (arXiv)

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.