# Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

arXiv: https://arxiv.org/abs/2305.16922 · HTML: https://mlsanity.com/paper/2305.16922/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 62 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 12 of 12)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty](https://mlsanity.com/paper/2011.07586/) (2020)
- [Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities](https://mlsanity.com/paper/1811.00648/) (2018)
- [Vox2Vox: 3D-GAN for Brain Tumour Segmentation](https://mlsanity.com/paper/2003.13653/) (2020)
- [Shifts 2.0: Extending The Dataset of Real Distributional Shifts](https://mlsanity.com/paper/2206.15407/) (2022)
- [Refacing: reconstructing anonymized facial features using GANs](https://mlsanity.com/paper/1810.06455/) (2018)
- [Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis](https://mlsanity.com/paper/2211.04825/) (2022)
- [Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust](https://mlsanity.com/paper/2209.10877/) (2022)

## Built on by (most cited first, 1 of 1)

- [Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential](https://mlsanity.com/paper/2501.18834/) (2025)

## Abstract (arXiv)

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re-identification protection but can significantly impact post-processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post-processing results by the price of higher re-identification risk and computational complexity. In the current study, we propose a novel method for anonymised face generation for defaced 3D T1-weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re-identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 seconds for face generation and is suitable for recovering consistent post-processing results after defacing.