Starck Sophie, Sideri-Lampretsa Vasiliki, Ritter Jessica J M, Zimmer Veronika A, Braren Rickmer, Mueller Tamara T, Rueckert Daniel
Artificial Intelligence in Healthcare and Medicine, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Institute of Diagnostic and Interventional Radiology, Technical University of Munich, School of Medicine, Munich, Germany.
Commun Med (Lond). 2024 Nov 19;4(1):237. doi: 10.1038/s43856-024-00670-0.
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.
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).
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.
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.
医学成像中可靠的参考数据大多难以获取。开发能够将个体患者数据与参考数据进行比较的工具,对于改善诊断成像具有很大潜力。群体图谱是医学成像中常用的一种工具,用于推动这一进程。当处理高度异质的数据集(如包含显著解剖变异的全身图像)时,构建此类图谱变得极具挑战性。
在这项工作中,我们提出了一种通过将群体划分为具有解剖学意义的亚组来为高度异质群体生成标准化全身图谱的流程。利用来自英国生物银行数据集的磁共振图像,我们创建了六个代表健康群体平均值的全身图谱。我们还对它们进行了去偏处理,从而获得该群体的真实表征。除了解剖图谱外,我们还生成了概率图谱,以捕捉整个人群中腹部脂肪(内脏脂肪和皮下脂肪)以及五个腹部器官(肝脏、脾脏、胰腺、左肾和右肾)的分布情况。
我们的流程有效地生成了具有临床适用性的高质量、真实的全身图谱。概率图谱显示,在图谱空间中,患有糖尿病和心血管疾病等病症的受试者与健康受试者之间存在脂肪分布差异。
通过这项工作,我们将构建的解剖图谱和标签图谱公开提供,期望它们能支持涉及全身磁共振图像的医学研究。