Using UK Biobank data to establish population-specific atlases from whole body MRI.

IF 5.4 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Sophie Starck, Vasiliki Sideri-Lampretsa, Jessica J M Ritter, Veronika A Zimmer, Rickmer Braren, Tamara T Mueller, Daniel Rueckert
{"title":"Using UK Biobank data to establish population-specific atlases from whole body MRI.","authors":"Sophie Starck, Vasiliki Sideri-Lampretsa, Jessica J M Ritter, Veronika A Zimmer, Rickmer Braren, Tamara T Mueller, Daniel Rueckert","doi":"10.1038/s43856-024-00670-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Reliable 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.</p><p><strong>Method: </strong>In 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).</p><p><strong>Results: </strong>Our 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.</p><p><strong>Conclusions: </strong>With 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>","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":"4 1","pages":"237"},"PeriodicalIF":5.4000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s43856-024-00670-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Reliable 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.

Method: In 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).

Results: Our 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.

Conclusions: With 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.

利用英国生物库数据,从全身核磁共振成像中建立特定人群图谱。
背景:医学成像中可靠的参考数据在很大程度上是不可用的。开发可将单个患者数据与参考数据进行比较的工具极有可能改进影像诊断。人群图谱是医学影像中常用的工具,可促进这一工作。在处理高度异构的数据集时,构建这样的图集尤其具有挑战性,例如包含显著解剖学差异的全身图像:在这项工作中,我们提出了一个管道,通过将人群划分为具有解剖意义的亚组,为高度异构的人群生成标准化的全身图集。利用英国生物库数据集的磁共振图像,我们创建了代表健康人群平均水平的六个全身图谱。此外,我们还对它们进行了不偏倚处理,从而获得了一个真实的人群代表。除了解剖图集外,我们还生成了概率图集,以捕捉整个人群的腹部脂肪(内脏和皮下脂肪)和五个腹部器官(肝脏、脾脏、胰腺、左肾和右肾)的分布情况:结果:我们的管道能有效生成高质量、逼真的全身图谱,具有临床应用价值。概率图集显示了患有糖尿病和心血管疾病等疾病的受试者与健康受试者在图集空间的脂肪分布差异:通过这项工作,我们公开了所构建的解剖和标签图谱,希望它们能为涉及全身磁共振图像的医学研究提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信