Medical image registration is a difficult problem. Not only a registration algorithm needs to capture both large and small scale image deformations, it also has to deal with global and local image intensity variations. In this paper we describe a new multiresolution elastic image registration method that challenges these difficulties in image registration. To capture large and small scale image deformations, we use both global and local affine transformation algorithms. To address global and local image intensity variations, we apply an image intensity standardization algorithm to correct image intensity variations. This transforms image intensities into a standard intensity scale, which allows highly accurate registration of medical images.
Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration
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）；Munich Center for Machine Learning（慕尼黑机器学习中心）；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）；German Cancer Consortium (DKTK)（德国癌症联盟）；School of Biomedical Engineering and 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）
Biomechanics-informed Non-rigid Medical Image Registration and its Inverse Material Property Estimation with Linear and Nonlinear Elasticity
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London（伦敦大学学院惠康/英国工程与物理科学研究委员会介入与外科科学中心）；Division of Surgery & Interventional Science, University College London（伦敦大学学院外科与介入科学系）；Institute of Medical and Biological Engineering, University of Leeds（利兹大学医学与生物工程研究所）
Mesh-to-raster based non-rigid registration of multi-modal images
Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University（亚琛工业大学计算工程科学高级研究院（AICES））；Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig – Institute of Technology and Hannover Medical School（布伦瑞克工业大学与汉诺威医学院彼得·L·赖歇茨医学信息学研究所）
DARE: A Deformable Adaptive Regularization Estimator for Learning-Based Medical Image Registration
Department of Mathematics, University of Innsbruck（数学系，因斯布鲁克大学）；Department of Radiology, Medical University of Innsbruck（放射学系，因斯布鲁克医学院）；Neuroimaging Research Core Facility, Medical University of Innsbruck（神经影像研究核心设施，因斯布鲁克医学院）
SemICP: Semantic Non-Rigid Point Cloud Registration with Elastic Energy Regularization
Department of Electrical and Computer Engineering, University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）；Faculty of Medicine, University of British Columbia（医学院，不列颠哥伦比亚大学）
A general technique for the estimation of farm animal body part weights from CT scans and its applications in a rabbit breeding program
Medicopus Nonprofit Ltd.（Medicopus 非营利有限公司）；Hungarian University of Agricultural and Life Sciences（匈牙利农业与生命科学大学）；Analytical Minds Ltd.（Analytical Minds 有限公司）
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
University of Bern（伯尔尼大学）；CentraleSupélec（中央理工-高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）；Groupe Hospitalier Cochin-Hôtel Dieu（科钦-主宫医院集团）；Université Paris Descartes（巴黎笛卡尔大学）；TheraPanacea
Biomechanical Constraints Assimilation in Deep-Learning Image Registration: Application to sliding and locally rigid deformations
Centre de Recherches en Cancérologie de Toulouse（图卢兹癌症研究中心）；INSERM（法国国家健康与医学研究院）；Institut de Mathématiques de Toulouse（图卢兹数学研究所）；CNRS（法国国家科学研究中心）；Université de Toulouse（图卢兹大学）；Institut Universitaire du Cancer – Oncopole Claudius Régaud（克劳迪乌斯·雷戈德肿瘤大学医院研究所）
