In a large-scale medical examination, the UK Biobank study has successfully imaged more than 32,000 volunteer participants with magnetic resonance imaging (MRI). Each scan is linked to extensive metadata, providing a comprehensive medical survey of imaged anatomy and related health states. Despite its potential for research, this vast amount of data presents a challenge to established methods of evaluation, which often rely on manual input. To date, the range of reference values for cardiovascular and metabolic risk factors is therefore incomplete. In this work, neural networks were trained for image-based regression to infer various biological metrics from the neck-to-knee body MRI automatically. The approach requires no manual intervention or direct access to reference segmentations for training. The examined fields span 64 variables derived from anthropometric measurements, dual-energy X-ray absorptiometry (DXA), atlas-based segmentations, and dedicated liver scans. With the ResNet50, the standardized framework achieves a close fit to the target values (median R^2 > 0.97) in cross-validation. Interpretation of aggregated saliency maps suggests that the network correctly targets specific body regions and limbs, and learned to emulate different modalities. On several body composition metrics, the quality of the predictions is within the range of variability observed between established gold standard techniques.
The UK Biobank Imaging Study has acquired medical scans of more than 40,000 volunteer participants. The resulting wealth of anatomical information has been made available for research, together with extensive metadata including measurements of liver fat. These values play an important role in metabolic disease, but are only available for a minority of imaged subjects as their collection requires the careful work of image analysts on dedicated liver MRI. Another UK Biobank protocol is neck-to-knee body MRI for analysis of body composition. The resulting volumes can also quantify fat fractions, …
Department of Surgical Sciences, Uppsala University（乌普萨拉大学外科学系）；Department of Information Technology, Uppsala University（乌普萨拉大学信息技术系）；Antaros Medical AB
UK Biobank (UKB) conducts large-scale examinations of more than half a million volunteers, collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Medical imaging of 100,000 subjects, with 70,000 follow-up sessions, enables measurements of organs, muscle, and body composition. With up to 170,000 mounting MR images, various methodologies are accordingly engaged in large-scale image analysis. This work presents an experimental inference engine that can automatically predict a comprehensive profile of subject metadata from UKB neck-to-knee body MRI. It was eval…
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；William Harvey Research Institute（威廉·哈维研究所）；Queen Mary University of London（伦敦玛丽女王大学）；Barts Health NHS Trust（巴茨健康国民保健信托基金）
We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify two small clusters which probably correspond to two pathological categories. Furth…
Automated Fetal Brain MRI Biometry in Healthy and Pathological Cases
Faculty of Medicine, University of Ljubljana（卢布尔雅那大学医学院）；University Medical Centre Ljubljana（卢布尔雅那大学医学中心）；Faculty of Electrical Engineering, University of Ljubljana（卢布尔雅那大学电气工程学院）
Along with rich health-related metadata, medical images have been acquired for over 40,000 male and female UK Biobank participants, aged 44-82, since 2014. Phenotypes derived from these images, such as measurements of body composition from MRI, can reveal new links between genetics, cardiovascular disease, and metabolic conditions. In this work, six measurements of body composition and adipose tissues were automatically estimated by image-based, deep regression with ResNet50 neural networks from neck-to-knee body MRI. Despite the potential for high speed and accuracy, these networks produce no…
Uppsala University（乌普萨拉大学）；Antaros Medical AB（安塔罗斯医疗公司）
Large-scale medical studies such as the UK Biobank examine thousands of volunteer participants with medical imaging techniques. Combined with the vast amount of collected metadata, anatomical information from these images has the potential for medical analyses at unprecedented scale. However, their evaluation often requires manual input and long processing times, limiting the amount of reference values for biomarkers and other measurements available for research. Recent approaches with convolutional neural networks for regression can perform these evaluations automatically. On magnetic resonan…
a Division of Medical Informatics, Department of Biomedical Engineering b Division of Statistics \& Machine Learning, Department of Computer；Information Science c Center for Social；Affective Neuroscience, Department of Biomedical；Clinical Sciences d Center for Medical Image Science；Visualization (CMIV) \ \" o ping University, Sweden
Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of the 3D volumes leads to reasonable test accuracy when predicting the age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20 - 50 seconds using a single GPU, which two orders of magnitude faster compared to a small 3D CNN. These results are …
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice. In this work we introduce a convolutional neural network model for identifying disease in temporal sequences of cardiac MR segmentations which is interpretable in terms of clinically familiar measurements. The model is based around a variational autoencoder, reducing the input into a low-dimensional latent space in which classification occurs. We then use the recently developed `conce…
Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI
Hawkes Institute（霍克斯研究所）；University College London（伦敦大学学院）；Institut Pasteur（巴斯德研究所）；Université Paris Cité（巴黎西岱大学）；AINOSTICS ltd.（AINOSTICS有限公司）；Fudan University（复旦大学）；Université Libre de Bruxelles (ULB)（布鲁塞尔自由大学）；University of Liège (ULiège)（列日大学）
Imperial College London（帝国理工学院）；Technical University Munich（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；MRC London Institute of Medical Sciences（MRC伦敦医学科学研究所）
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion fi…
Uppsala University（乌普萨拉大学）；Antaros Medical AB（安塔罗斯医疗公司）
The UK Biobank is collecting extensive data on health-related characteristics of over half a million volunteers. The biological samples of blood and urine can provide valuable insight on kidney function, with important links to cardiovascular and metabolic health. Further information on kidney anatomy could be obtained by medical imaging. In contrast to the brain, heart, liver, and pancreas, no dedicated Magnetic Resonance Imaging (MRI) is planned for the kidneys. An image-based assessment is nonetheless feasible in the neck-to-knee body MRI intended for abdominal body composition analysis, wh…
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decades considerable effort has been put into accelerating scan times without compromising image quality or the accuracy of derived results. In this paper, we present a fully-automated, quality-controlled integrated framework for reconstruction, segmentation and downstream analysis of undersampled cine CMR data. The framework enables active acquisition of radial k-space data, in which acquisition can be stopped as soon as …
Interpretable Representation Learning of Cardiac MRI via Attribute Regularization
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；Helmholtz AI, Helmholtz Munich（亥姆霍兹慕尼黑亥姆霍兹人工智能研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Imperial College London（帝国理工学院）；National University of Defense Technology（国防科技大学）；MRC London Institute of Medical Sciences（英国医学研究委员会伦敦医学科学研究所）；Technical University Munich（慕尼黑工业大学）
3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space, which ignore the fact that motion estimation is mainly relevant and useful within the object of interest, e.g., the heart. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short- and long-axis CMR images. By developing a …
The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Sagol Brain Institute, Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心萨戈尔脑研究所）；Tel Aviv University（特拉维夫大学）；Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心）；University of Ottawa（渥太华大学）；Children's Hospital of Eastern Ontario（东安大略儿童医院）；Shaare Zedek Medical Center（沙雷泽德克医疗中心）
Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements…
School of Biomedical Engineering & Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学院）；Technical University of Munich, Germany（慕尼黑工业大学）；Biomedical Image Analysis Group, Imperial College London, UK（帝国理工学院生物医学图像分析组）；Department of Adult and Paediatric Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, UK（盖伊和圣托马斯国民健康服务信托基金会成人及儿科心脏病科）；Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Portugal（科英布拉大学科英布拉生物医学成像与转化研究学院）；Helmholtz Center Munich, Germany（慕尼黑亥姆霍兹中心）
Cine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-space profiles to ensure good image quality. Several undersampling-based reconstruction techniques have been proposed during the last decades to speed up cine cardiac MRI acquisition. However, the undersampling factor is commonly fixed to conservative values before acquisition to ensure diagnostic image quality, potentially leading to unnecessarily long scan times. In this paper, we propose an end-to-end quality-aware c…
A Generalizable Deep Learning System for Cardiac MRI
Division of Cardiovascular Surgery, Department of Surgery, University of Pennsylvania（宾夕法尼亚大学心血管外科 division）；Department of Cardiothoracic Surgery, Stanford University（斯坦福大学心胸外科 department）；Department of Radiology, Medstar Georgetown University Hospital（梅奥医疗中心乔治城大学医院放射科 department）；Department of Radiology and Biomedical Imaging, University of California, San Francisco（加州大学旧金山分校放射科和生物医学成像 department）；Bunkerhill Health, San Francisco（旧金山布纳克希尔健康机构）；Department of Radiology, University of Pennsylvania（宾夕法尼亚大学放射科 department）；Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania（宾夕法尼亚大学心血管医学 division）；Division of Cardiovascular Medicine, Department of Medicine, Genetics, and Biomedical Data Science, Stanford University（斯坦福大学心血管医学 division）；Department of Radiology, Medicine, and Biomedical Data Science, Stanford University（斯坦福大学放射科、医学和生物医学数据科学 department）
Surface-volume self-supervised representation learning of brain MRI for genetic discovery
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Yale School of Medicine（耶鲁大学医学院）；Texas A&M University（德克萨斯A&M大学）；Stanford University School of Medicine（斯坦福大学医学院）
Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI
Berlin Institute of Health at Charité - Universitätsmedizin Berlin（柏林健康研究所，柏林夏里特医学院）；Health Data Science Unit, Heidelberg University Hospital and BioQuant（海德堡大学医院与BioQuant健康数据科学部）；Intelligent Medicine Institute, Fudan University（复旦大学智能医学研究所）；Department of Mathematics and Computer Science, Freie Universität Berlin（柏林自由大学数学与计算机科学系）
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT
Department of Radiology, Charité - Universitätsmedizin Berlin（柏林查理医院放射科）；Freie Universität Berlin（柏林自由大学）；Humboldt Universität zu Berlin（柏林洪堡大学）；Diagnostic Image Analysis Group, Radboud University Medical Center（拉德堡德大学医学中心诊断图像分析组）；School of Medicine and Health, Klinikum rechts der Isar, TUM University Hospital（医学与健康学院，莱比锡大学医院）；Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg（埃朗根大学医院放射科，埃朗根-纽伦堡弗里德里希-亚历山大大学）；Laboratory for Computer Aided Medical Procedures, Technical University of Munich（慕尼黑技术大学计算机辅助医疗程序实验室）；Chair for AI in Medicine and Healthcare, Klinikum rechts der Isar, Technical University Munich（医学与健康中的AI教授职位，莱比锡大学医院，慕尼黑技术大学）
Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants
School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）；The Cardiac Electro-Mechanics Research Group, Imperial College London（伦敦帝国理工学院心脏电机械学研究组）；The Alan Turing Institute, London（伦敦艾伦·图灵研究所）；Department of Cardiology, University Medical Center Utrecht（乌得勒支大学医学中心心内科部）；ETH Zurich（苏黎世联邦理工学院）
VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank
TUM University Hospital（慕尼黑工业大学附属医院）；Technical University of Munich（慕尼黑工业大学）；University Medicine Greifswald（格赖夫斯瓦尔德大学医学中心）；University Medical Center Freiburg（弗赖堡大学医学中心）；University of Freiburg（弗赖堡大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Imperial College London（伦敦帝国理工学院）
FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI
German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心）；University of Bonn（波恩大学）；Massachusetts General Hospital（马萨诸塞总医院）；Harvard Medical School（哈佛医学院）
National Institutes of Health（美国国立卫生研究院）；Imperial College London（帝国理工学院）；GlaxoSmithKline（葛兰素史克）
We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explainability of the inter-domain transformation and is often conducted at the feature-level in the latent space. We employ ISTNs for DA at the image-level which constrains transformations to explainable appearance and shape changes. As proof-of-concept we demonstrate that ISTNs can be trained adversarially on a classification problem with simulated 2D data. For real-data validation, we construc…
A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI
Swiss Data Science Center, EPFL and ETH Zürich（瑞士数据科学中心，EPFL 和 ETH 苏黎世）；Diagnostic and Interventional Radiology, University Hospital Zürich（诊断与介入放射学，苏黎世大学医院）；Institute for Biomedical Engineering, University and ETH Zürich（生物医学工程研究所，苏黎世大学和 ETH 苏黎世）；Biomedical Informatics Group, ETH Zürich（生物医学信息学小组，ETH 苏黎世）；Division of Preventive Medicine, Brigham and Women’s Hospital / Harvard Medical School（预防医学部，布莱根妇女医院/哈佛医学院）
Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Medicine, Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学伊萨尔河畔右岸医院医学院）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）；Department of Computing, Imperial College London（帝国理工学院计算系）；Department of Computer Science, University of Sheffield（谢菲尔德大学计算机科学系）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Institute for Signals, Systems, and Computational Intelligence, sinc(i) CONICET-UNL（信号、系统与计算智能研究所，sinc(i) CONICET-UNL）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds（生物医学计算成像与模拟技术中心（CISTIB），计算学院，莱斯特大学）；Christabel Pankhurst Institute, The University of Manchester（克里斯特贝尔·潘克赫斯特研究所，曼彻斯特大学）；Centre for Computational Imaging and Modelling in Medicine (CIMIM), Department of Computer Science, School of Engineering, and Division of Informatics Imaging and Data Science, School of Health Sciences, The University of Manchester（医学计算成像与建模中心（CIMIM），计算机科学系，工程学院，信息学成像与数据科学系，健康科学学院，曼彻斯特大学）；Medical Imaging Research Centre (MIRC), Department of Cardiovascular Sciences, KU Leuven（医学影像研究中心（MIRC），心血管科学系，鲁汶大学）
Three-dimensional end-to-end deep learning for brain MRI analysis
Technical University Dresden（德累斯顿工业大学）；University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院及医学院）；National Center for Tumor Diseases Dresden（德累斯顿国家肿瘤疾病中心）；University Hospital Heidelberg（海德堡大学医院）；National Center for Tumor Diseases（国家肿瘤疾病中心）；University Hospital Aachen（亚琛大学医院）；Carl Gustav Carus University Hospital, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院）
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与医学中的研究中心，技术大学慕尼黑（TUM）及慕尼黑技术大学医院）；School of Medicine, Klinikum rechts der Isar, TUM, Germany（医学院，右岸克里克医院，TUM，德国）；Department of Computing, Imperial College London, UK（计算学院，伦敦帝国学院，英国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），慕尼黑，德国）
University of Westminster（威斯敏斯特大学）；Calico Life Sciences LLC（Calico生命科学有限责任公司）
Dixon MRI is widely used for body composition studies. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed on the scanner, making the data difficult to analyse. The most common artifact are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which can be wasteful and lead to unintended biases. The UK Biobank is acquiring Dixon MRI for over 100,000 participants, and thousands of swaps will occur. If those go u…
MIMM-X: Disentangling Spurious Correlations for Medical Image Analysis
Medical Image and Data Analysis, University Hospital of Tübingen, Germany（医学图像与数据分析，图宾根大学医院，德国）；Institute for Signal Processing and System Theory, University of Stuttgart, Germany（信号处理与系统理论研究所，斯图加特大学，德国）；Stanford University, Department of Radiology, Stanford, USA（斯坦福大学放射科，斯坦福，美国）
InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model
St. Francis Xavier University（圣弗朗西斯泽维尔大学）；Martinos Center for Biomedical Imaging（马蒂诺生物医学成像中心）；Massachusetts General Hospital（麻省总医院）；Nova Scotia Health Authority（新斯科舍省卫生局）；King’s College London（伦敦国王学院）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
University of Southern California（南加州大学）；Information Sciences Institute（信息科学研究所）；Mark and Mary Stevens Institute for Neuroimaging and Informatics（马克与玛丽·史蒂文斯神经影像与信息学研究所）；Keck School of Medicine（凯克医学院）
Deep Learning for neuroimaging data is a promising but challenging direction. The high dimensionality of 3D MRI scans makes this endeavor compute and data-intensive. Most conventional 3D neuroimaging methods use 3D-CNN-based architectures with a large number of parameters and require more time and data to train. Recently, 2D-slice-based models have received increasing attention as they have fewer parameters and may require fewer samples to achieve comparable performance. In this paper, we propose a new architecture for BrainAGE prediction. The proposed architecture works by encoding each 2D sl…
