Deep learning cardiac motion analysis for human survival prediction
National Heart and Lung Institute, Imperial College London, UK（英国帝国理工学院国家心肺研究所）；Department of Computing, Imperial College London, UK（英国帝国理工学院计算机系）；Imperial College Healthcare NHS Trust, London, UK（英国伦敦帝国理工学院医疗NHS信托基金）；Division of Experimental Medicine, Department of Medicine, Imperial College London, UK（英国帝国理工学院医学系实验医学部）；National Heart Centre Singapore, Singapore, and Duke-NUS Graduate Medical School, Singapore（新加坡国家心脏中心和新加坡杜克-新加坡国立大学医学院）
Ghalib A. Bello, Timothy J. W. Dawes, Jinming Duan, Carlo Biffi, Antonio de Marvao, Luke S. G. E. Howard, J. Simon R. Gibbs, Martin R. Wilkins, Stuart A. Cook, … 展开作者
Ghalib A. Bello, Timothy J. W. Dawes, Jinming Duan, Carlo Biffi, Antonio de Marvao, Luke S. G. E. Howard, J. Simon R. Gibbs, Martin R. Wilkins, Stuart A. Cook, Daniel Rueckert, Declan P. O'Regan
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.
Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning
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）
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank
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）
MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI
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）
MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI
School of Computer Science, University of Birmingham（英国伯明翰大学计算机科学学院）；Division of Informatics, Imaging and Data Sciences, University of Manchester（曼彻斯特大学信息学与数据科学系）；Centre for Computational Imaging and Modelling in Medicine, University of Manchester（曼彻斯特大学医学计算成像与建模中心）；Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（广东省计算机视觉与虚拟现实技术重点实验室，深圳先进技术研究院，中国科学院）；MRC Laboratory of Medical Sciences, Imperial College London（伦敦帝国理工学院医学科学实验室）；UKRI CDT in AI for Healthcare, Imperial College London（伦敦帝国理工学院人工智能与医疗健康CDT）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机科学系）
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI
University of Nottingham（诺丁汉大学）
心脏磁共振成像（MRI）在心功能分析中起着重要作用。然而，由于屏气困难，采集通常伴随有运动伪影，特别是对于急性症状患者。因此，有必要对心脏MRI的质量进行评估，以便进一步分析。耗时的人工分类不利于构建端到端的计算机辅助诊断系统。为了克服这个问题，在这项工作中提出了一个自动心脏MRI质量估计框架，使用集成和迁移学习。多个预先训练的模型被初始化，并在从训练数据中采样的二维图像块上进行微调。在模型推理过程中，来自这些模型的决策被汇总以做出最终预测。该框架已在CMRxMotion Grand Challenge（MICCAI 2022）数据集上进行了评估，该数据集较小，多类且不平衡。它在训练集（5倍交叉验证）和验证集上分别实现了78.8%和70.0%的分类准确率。CMRxMotion组织者还在独立测试集上对最终训练模型进行了评估，其分类准确率为72.5%，Cohen's Kappa为0.6309（在这项重大挑战中排名第一）。我们的代码可以在Github上找到：https://github.com/ruizhe-l/CMRxMotion。
Reconstruction of Cardiac Cine MRI Using Motion-Guided Deformable Alignment and Multi-Resolution Fusion
School of Health Science and Engineering, University of Shanghai for Science and Technology（上海理工大学健康科学与工程学院）；ToolSensing Technologies Co., Ltd（拓深科技有限公司）；School of Medical Instruments, Shanghai University of Medicine and Health Sciences（上海健康医学院医疗器械学院）；Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University（华东师范大学通信与电子工程学院多维信息处理上海重点实验室）
Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；Department of Computer Science, National University of Singapore, Singapore（新加坡国立大学计算机科学系）；Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand（奥克兰大学生物工程研究所）；School of Biomedical Engineering and 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校）；Department of Medicine, National University of Singapore, Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore, Singapore（新加坡国立心脏中心心血管科）
Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis
University of Virginia（弗吉尼亚大学）；University of Chicago（芝加哥大学）；Ohio State University（俄亥俄州立大学）；St. Francis Hospital & Heart Center（圣弗朗西斯医院及心脏中心）；Hartford HealthCare（哈特福德医疗集团）；Instituto do Coração (InCor)（心脏研究所（InCor））；Atlantic Health System（大西洋健康系统）；Sociedad Italiana de Beneficencia (Hospital Italiano)（意大利慈善协会（意大利医院））
A latent ODE approach to full-cycle biventricular cardiac motion modeling from cine 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）
End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI
School of Computer and Communication Science, EPFL（计算机与通信科学学院，瑞士联邦理工学院）；ELLIS Institute Finland, Aalto University, Finland（芬兰ELLIS研究所，阿尔托大学，芬兰）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Center for Interventional MRI, CHUV（介入MRI中心，CHUV）
Contrast-Free Myocardial Scar Segmentation in Cine MRI using Motion and Texture Fusion
University of Oxford（牛津大学）；University of Southampton（南安普顿大学）；National University of Singapore（新加坡国立大学）；Fudan University（复旦大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）
Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg（生物医学工程人工智能系，埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Department of Biomedical Engineering, The Ohio State University（生物医学工程系，俄亥俄州立大学）；Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG（研究与临床转化，磁共振，西门子医疗有限公司）；Division of Cardiovascular Medicine, The Ohio State University（心血管医学系，俄亥俄州立大学）
Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins
Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；Department of Medicine, National University of Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore（新加坡国立心脏中心心内科部）
CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；School of Automation, Southeast University, Nanjing, China（东南大学自动化学院）；Department of Medicine, National University of Singapore, Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore, Singapore（新加坡国立心脏中心心内科部）
本文提出 CineMesh4D，一种端到端的 4D 全心脏重建方法，通过跨域映射从多视角 2D cine MRI 直接重建患者特定的全心脏网格，提升重建质量和运动一致性。
Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors
University of California, Los Angeles（加州大学洛杉矶分校）；Nuffield College, Oxford University（牛津大学纳菲尔德学院）；Oxford Man Institute, Oxford University（牛津大学牛津曼研究所）；Alan Turing Institute（艾伦·图灵研究所）
Unsupervised 4D Cardiac Motion Tracking with Spatiotemporal Optical Flow Networks
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；DJI Technology Co., Ltd.（大疆创新科技有限公司）；Shenzhen University of Advanced Technology（深圳理工大学）
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method
Rutgers University（罗格斯大学）；SenseBrain Research（SenseBrain研究院）；University of Arkansas for Medical Sciences（阿肯色大学医学院）；Lund University（隆德大学）；New York University（纽约大学）
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
BCN MedTech, Department of Engineering, Universitat Pompeu Fabra（BCN MedTech，工程系，庞培法布拉大学）；European Commission, Joint Research Centre (JRC)（欧洲委员会，联合研究中心（JRC））
A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland
School of Mathematics, Statistics and Physics（数学、统计与物理学院）；Newcastle University（新卡茨大学）；Civil and Environmental Engineering（土木与环境工程）；Centre for Maritime Research（海事研究与实验中心）；Mechanical and Aerospace Engineering（机械与航空航天工程）
