# A method for tissue-mask supported whole-body image registration in the UK Biobank

arXiv: https://arxiv.org/abs/2512.02702 · HTML: https://mlsanity.com/paper/2512.02702/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network](https://mlsanity.com/paper/1704.06065/) (2017)
- [TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI](https://mlsanity.com/paper/2405.19492/) (2024)
- [uniGradICON: A Foundation Model for Medical Image Registration](https://mlsanity.com/paper/2403.05780/) (2024)
- [VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank](https://mlsanity.com/paper/2406.00125/) (2024)
- [MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT](https://mlsanity.com/paper/2405.06463/) (2024)
- [Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images](https://mlsanity.com/paper/1810.08427/) (2018)
- [Using UK Biobank data to establish population-specific atlases from whole body MRI](https://mlsanity.com/paper/2308.14365/) (2023)

## Abstract (arXiv)

The UK Biobank is a large-scale study collecting imaging and non-imaging health data. Robust and accurate inter-subject image registration of these whole-body MR images would enable their spatial standardization, and region/voxel-wise correlation analysis of non-imaging data with image-derived parameters. We propose a sex-stratified inter-subject whole-body MR image registration approach that uses subcutaneous adipose tissue- and muscle-masks from the state-of-the-art VIBESegmentator method to augment intensity-based graph-cut registration. The proposed mask-supported method was evaluated on 4000 subjects by comparisons to an intensity-only method as well as two other methods, uniGradICON and MIRTK. The evaluation included Jacobian Determinant (JD) folding frequency, Dice scores, and voxel-wise label error frequency. MRSegmentator and TotalSegmentator were also used for independent Dice score evaluations. Additionally, voxel-wise correlation between age and both fat content and tissue volume was studied to exemplify the usefulness for medical research. The proposed method showed lower frequency of JD folding when compared to the intensity-based method. The mask-supported method exhibited a mean Dice score of 0.77 / 0.74 when evaluated on the VIBESegmentator masks for males / females, respectively. In comparison to the intensity-only method, the mean values were 6 pp higher for both sexes, and the label error frequency decreased in most regions. These differences were 9pp / 8pp against uniGradICON and 12pp / 13pp against MIRTK. The proposed method showed higher mean Dice scores in comparison to the other methods when evaluated on MRSegmentator and TotalSegmentator. The age-correlation maps were less noisy and showed higher anatomical alignment. In conclusion, the image registration method using two tissue masks improves whole-body registration of UK Biobank images.