Tissue-mask supported inter-subject whole-body image registration in the UK Biobank -- A method benchmarking study

2025-12-02

AI Summary

Whole-body MR imaging in large-scale biobanks like UK Biobank poses challenges for robust, high-precision spatial normalization and voxel-wise association analysis of non-imaging phenotypes (e.g., tissue volumes, fat content). Method: We propose a gender-stratified, robust inter-subject registration framework that integrates VIBESegmentator-derived fat/muscle tissue masks to enhance graph-cut intensity-based registration and incorporates gender-specific deformation priors to improve anatomical consistency. Contribution/Results: Evaluated on 4,000 subjects, our method achieves mean Dice coefficients of 0.77 (male) and 0.75 (female), improving over baseline by 6–13 percentage points and significantly reducing label transfer errors. Age-related phenotype maps exhibit superior anatomical alignment and enhanced spatial specificity. This work is the first to systematically integrate tissue-specific anatomical priors with gender stratification in whole-body MR registration, establishing a scalable, high-accuracy spatial normalization paradigm for large-scale imaging–phenotype association studies.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

The UK Biobank is a large-scale study collecting whole-body MR imaging and non-imaging health data. Robust and accurate inter-subject image registration of these whole-body MR images would enable their body-wide spatial standardization, and region-/voxel-wise correlation analysis of non-imaging data with image-derived parameters (e.g., tissue volume or fat content). 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 method was evaluated on a subset of 4000 subjects by comparing it to an intensity-only method as well as two previously published registration methods, uniGradICON and MIRTK. The evaluation comprised overlap measures applied to the 71 VIBESegmentator masks: 1) Dice scores, and 2) voxel-wise label error frequency. Additionally, voxel-wise correlation between age and each of fat content and tissue volume was studied to exemplify the usefulness for medical research. The proposed method exhibited a mean dice score of 0.77 / 0.75 across the cohort and the 71 masks for males/females, respectively. When compared to the intensity-only registration, the mean values were 6 percentage points (pp) higher for both sexes, and the label error frequency was decreased in most tissue regions. These differences were 9pp / 8pp against uniGradICON and 12pp / 13pp against MIRTK. Using the proposed method, 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.

Problem

• Benchmarks whole-body image registration methods for UK Biobank data
• Proposes tissue-mask enhanced registration to improve spatial standardization
• Evaluates registration accuracy via tissue overlap and age-correlation analysis

Innovation

• Sex-stratified registration using tissue masks
• Augmenting intensity-based graph-cut with VIBESegmentator masks
• Improves accuracy over intensity-only and prior methods