# Flexible Bayesian Modelling for Nonlinear Image Registration

arXiv: https://arxiv.org/abs/2006.02338 · HTML: https://mlsanity.com/paper/2006.02338/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

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

- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [Model-based multi-parameter mapping](https://mlsanity.com/paper/2102.01604/) (2021)
- [Factorisation-based Image Labelling](https://mlsanity.com/paper/2111.10326/) (2021)
- [BREIT: A Framework for Brain Stroke Reconstruction using Multi-Frequency 3D EIT](https://mlsanity.com/paper/2606.28787/) (2026)

## Abstract (arXiv)

We describe a diffeomorphic registration algorithm that allows groups of images to be accurately aligned to a common space, which we intend to incorporate into the SPM software. The idea is to perform inference in a probabilistic graphical model that accounts for variability in both shape and appearance. The resulting framework is general and entirely unsupervised. The model is evaluated at inter-subject registration of 3D human brain scans. Here, the main modeling assumption is that individual anatomies can be generated by deforming a latent 'average' brain. The method is agnostic to imaging modality and can be applied with no prior processing. We evaluate the algorithm using freely available, manually labelled datasets. In this validation we achieve state-of-the-art results, within reasonable runtimes, against previous state-of-the-art widely used, inter-subject registration algorithms. On the unprocessed dataset, the increase in overlap score is over 17%. These results demonstrate the benefits of using informative computational anatomy frameworks for nonlinear registration.