| Title: | Using UK Biobank data to establish population-specific atlases from whole body MRI |
| Journal: | Communications Medicine |
| Published: | 19 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39562746/ |
| DOI: | https://doi.org/10.1038/s43856-024-00670-0 |
| URL: | https://www.nature.com/articles/s43856-024-00670-0.pdf |
Publication 14098
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
BackgroundReliable reference data in medical imaging is largely unavailable. Developing tools that allow for the comparison of individual patient data to reference data has a high potential to improve diagnostic imaging. Population atlases are a commonly used tool in medical imaging to facilitate this. Constructing such atlases becomes particularly challenging when working with highly heterogeneous datasets, such as whole-body images, which contain significant anatomical variations.MethodIn this work, we propose a pipeline for generating a standardised whole-body atlas for a highly heterogeneous population by partitioning the population into anatomically meaningful subgroups. Using magnetic resonance images from the UK Biobank dataset, we create six whole-body atlases representing a healthy population average. We furthermore unbias them, and this way obtain a realistic representation of the population. In addition to the anatomical atlases, we generate probabilistic atlases that capture the distributions of abdominal fat (visceral and subcutaneous) and five abdominal organs across the population (liver, spleen, pancreas, left and right kidneys).ResultsOur pipeline effectively generates high-quality, realistic whole-body atlases with clinical applicability. The probabilistic atlases show differences in fat distribution between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects in the atlas space.ConclusionsWith this work, we make the constructed anatomical and label atlases publically available, with the expectation that they will support medical research involving whole-body MR images.</p>
7 Authors
- Sophie Starck
- Vasiliki Sideri-Lampretsa
- Jessica J. M. Ritter
- Veronika A. Zimmer
- Rickmer Braren
- Tamara T. Mueller
- Daniel Rueckert
1 Application
| Application ID | Title |
|---|---|
| 87802 | Assessment of frailty and biological age using multi-modal deep learning |
Enabling scientific discoveries that improve human health