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

arXiv: https://arxiv.org/abs/2406.06372 · HTML: https://mlsanity.com/paper/2406.06372/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 25 of its 56 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 25 of 25)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [Effective Data Augmentation With Diffusion Models](https://mlsanity.com/paper/2302.07944/) (2023)
- [Data Augmentation Generative Adversarial Networks](https://mlsanity.com/paper/1711.04340/) (2017)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [Medical Image Synthesis with Context-Aware Generative Adversarial Networks](https://mlsanity.com/paper/1612.05362/) (2016)
- [IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis](https://mlsanity.com/paper/1807.06358/) (2018)
- [HyperMorph: Amortized Hyperparameter Learning for Image Registration](https://mlsanity.com/paper/2101.01035/) (2021)
- [Conditional Deformable Image Registration with Convolutional Neural Network](https://mlsanity.com/paper/2106.12673/) (2021)
- [Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data](https://mlsanity.com/paper/1912.03681/) (2019)
- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder](https://mlsanity.com/paper/2012.13253/) (2020)
- [Point Cloud Diffusion Models for Automatic Implant Generation](https://mlsanity.com/paper/2303.08061/) (2023)
- [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)
- [High-Resolution Cranial Defect Reconstruction by Iterative, Low-Resolution, Point Cloud Completion Transformers](https://mlsanity.com/paper/2308.03813/) (2023)
- [Cranial Implant Design via Virtual Craniectomy with Shape Priors](https://mlsanity.com/paper/2009.13704/) (2020)
- [Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation](https://mlsanity.com/paper/2108.05269/) (2021)
- [Data Augmentation with norm-VAE for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2012.00848/) (2020)
- [Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects](https://mlsanity.com/paper/2308.04883/) (2023)
- [Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement](https://mlsanity.com/paper/2009.10769/) (2020)
- [Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders](https://mlsanity.com/paper/2404.13106/) (2024)

## Built on by (most cited first, 2 of 2)

- [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025)
- [Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement](https://mlsanity.com/paper/2411.17342/) (2024)

## Abstract (arXiv)

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.