Deep Implicit Functions (DIFs) represent 3D geometry with continuous signed distance functions learned through deep neural nets. Recently DIFs-based methods have been proposed to handle shape reconstruction and dense point correspondences simultaneously, capturing semantic relationships across shapes of the same class by learning a DIFs-modeled shape template. These methods provide great flexibility and accuracy in reconstructing 3D shapes and inferring correspondences. However, the point correspondences built from these methods do not intrinsically preserve the topology of the shapes, unlike mesh-based template matching methods. This limits their applications on 3D geometries where underlying topological structures exist and matter, such as anatomical structures in medical images. In this paper, we propose a new model called Neural Diffeomorphic Flow (NDF) to learn deep implicit shape templates, representing shapes as conditional diffeomorphic deformations of templates, intrinsically preserving shape topologies. The diffeomorphic deformation is realized by an auto-decoder consisting of Neural Ordinary Differential Equation (NODE) blocks that progressively map shapes to implicit templates. We conduct extensive experiments on several medical image organ segmentation datasets to evaluate the effectiveness of NDF on reconstructing and aligning shapes. NDF achieves consistently state-of-the-art organ shape reconstruction and registration results in both accuracy and quality. The source code is publicly available at https://github.com/Siwensun/Neural_Diffeomorphic_Flow--NDF.
Diffeomorphic Multi-Resolution Deep Learning Registration for Applications in Breast MRI
Auckland Bioengineering Institute（奥克兰生物工程研究所）；University of Auckland（奥克兰大学）；Faculty of Medical and Health Sciences（医学与健康科学学院）；MGH/HST Martinos Center for Biomedical Imaging（麻省总医院/哈佛健康科学技术生物医学成像马蒂诺斯中心）；Harvard Medical School（哈佛医学院）；MIT（麻省理工学院）
NPBDREG: Uncertainty Assessment in Diffeomorphic Brain MRI Registration using a Non-parametric Bayesian Deep-Learning Based Approach
Technion – Israel Institute of Technology（以色列理工学院）
基于深度神经网络（DNN）的图像配准算法中的不确定性量化在用于临床应用的图像配准算法的部署中起着关键作用，所述临床应用例如手术规划、术中引导、疾病进展或治疗功效的纵向监测以及面向研究的处理管道。当前用于基于DNN的图像配准算法中的不确定性估计的可用方法可能导致次优临床决策，这是由于对于配准潜在空间的假定参数分布的配准主干的不确定性的潜在不准确估计。我们引入NPBDREG，这是一个完全非参数贝叶斯框架，用于在基于DNN的可变形图像配准中进行不确定性估计，方法是将Adam优化器与随机梯度Langevin动力学（SGLD）相结合，通过后验采样来表征底层后验分布。因此，它有可能提供与分布外数据高度相关的不确定性估计。我们证明了NPBDREG的附加值，与基线概率VoxelMorph模型（PrVXM）相比，使用来自四个公开数据库的$390$图像对进行脑MRI图像配准：MGH10，CMUC 12，ISBR 18和LPBA40。NPBDREG显示预测不确定性与分布外数据（$r>0.95$ vs. $r<0.5$）的相关性更好，配准精度（Dice评分，$0.74$ vs. $0.69$，$p \ll 0.01$）提高了7.3%，配准平滑度提高了18%（变形场中折叠百分比，0.014 vs. 0.017，$p \ll 0.01$）。最后，NPBDREG证明了一个更好的泛化能力的数据破坏了混合结构噪声（…
Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Field
University of Engineering and Technology, Vietnam National University（越南国立大学工程技术大学）；VinUniversity（文梅大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling
Medical Imaging Research Center, UZ Leuven（UZ Leuven医学成像研究中心）；Department of Electrical Engineering, ESAT/PSI, KU Leuven（KU Leuven电气工程系（ESAT/PSI））；Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, University of Pittsburgh（匹兹堡大学口腔与颅面科学系颅面和牙科遗传学中心）；Department of Computing, Imperial College London（帝国理工学院计算机系）；IDSIA, USI Lugano（卢加诺大学IDSIA研究所）；Twitter（推特公司）；Department of Human Genetics, KU Leuven（KU Leuven人类遗传学系）
ACSGRegNet: A Deep Learning-based Framework for Unsupervised Joint Affine and Diffeomorphic Registration of Lumbar Spine CT via Cross- and Self-Attention Fusion
Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction
Universidad Politécnica de Madrid（马德里理工大学）；Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络中心（生物工程、生物材料与纳米医学））；King’s College London（伦敦国王学院）
TransMorph: Transformer for unsupervised medical image registration
Johns Hopkins Medical Institutes（约翰·霍普金斯医学院）；Johns Hopkins University（约翰·霍普金斯大学）；NVIDIA Corporation（英伟达公司）；Duke University Medical Center（杜克大学医学中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
School of Computer and Electrical Engineering, Hunan University of Arts and Science（湖南文理学院计算机与电气工程学院）；Furong College, Hunan University of Arts and Science（湖南文理学院芙蓉学院）；The University of Hong Kong（香港大学）
MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision
University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine（佩雷尔曼医学院）；Center for Biomedical Image Computing and Analytics (CBICA)（生物医学图像计算与分析中心（CBICA））
NimbleReg: A light-weight deep-learning framework for diffeomorphic image registration
Hawkes Institute \& Department of Computer Science, University College London, London, UK AINOSTICS ltd., Manchester, UK ESIR, Universit\'e de Rennes, Rennes, France
Harvard Medical School（哈佛医学院）；INRIA Sophia Antipolis-Méditerranée（法国国家信息与自动化研究所索菲亚-安提波利斯-地中海中心）；University of North Carolina（北卡罗来纳大学）；Massachusetts Institute of Technology（麻省理工学院）
Volumetrically Consistent Implicit Atlas Learning via Neural Diffeomorphic Flow for Placenta MRI
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）；Boston Children’s Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Computer Science and Artificial Intelligence Lab, MIT（麻省理工学院计算机科学与人工智能实验室）；Martinos Center for Biomedical Imaging, Massachusetts General Hospital, HMS（哈佛医学院麻省总医院马蒂诺生物医学成像中心）；School of Electrical and Computer Engineering, Cornell University（康奈尔大学电子与计算机工程学院）
Factored Levenberg-Marquardt for Diffeomorphic Image Registration: An efficient optimizer for FireANTs
Department of Computer and Information Science（计算机与信息科学系）；University of Pennsylvania（宾夕法尼亚大学）；Department of Electrical and Systems Engineering（电气与系统工程系）；Department of Radiology & Penn Image Computing and Science Laboratory (PICSL)（放射科及宾夕法尼亚大学图像计算与科学实验室（PICSL））；Perelman School of Medicine, University of Pennsylvania（佩尔曼医学学院，宾夕法尼亚大学）
A Symmetric Dynamic Learning Framework for Diffeomorphic Medical Image Registration
Xiangtan University（湘潭大学）；University of Strathclyde（思克莱德大学）；Nankai University（南开大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Manchester（曼彻斯特大学）
Unsupervised learning of spatially varying regularization for diffeomorphic image registration
Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, MD, USA（放射科与放射科学系，约翰霍普金斯医学院，马里兰州，美国）；Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, MD, USA（图像分析与通信实验室，电气与计算机工程系，约翰霍普金斯大学，马里兰州，美国）；Department of Electrical and Computer Engineering, Vanderbilt University, TN, USA（电气与计算机工程系，范德比尔特大学，田纳西州，美国）；NVIDIA Corporation, Santa Clara, CA, USA（英伟达公司，圣克拉拉，加利福尼亚州，美国）
A Bi-variant Variational Model for Diffeomorphic Image Registration with Relaxed Jacobian Determinant Constraints
School of Mathematics and Computational Science（数学与计算科学学院）；University of Strathclyde（思克莱德大学）；Academy of Mathematics and Systems Science（数学与系统科学研究院）；Chinese Academy of Sciences（中国科学院）；National Center for Applied Mathematics in Hunan（湖南省应用数学中心）；Xiangtan University（湘潭大学）
VPREG: An Optimal Control Formulation for Diffeomorphic Image Registration Based on the Variational Principle Grid Generation Method
Department of Pharmacology and Neuroscience, University of North Texas Health Science Center（药理学与神经科学系，北德克萨斯健康科学中心大学）；Department of Mathematics, University of Texas at Arlington（数学系，德克萨斯大学阿灵顿分校）；Department of Mathematics, University of Houston（数学系，休斯顿大学）
Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond
Dalian University of Technology（大连理工大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Children’s Hospital of Philadelphia（费城儿童医院）；University of Pennsylvania（宾夕法尼亚大学）
Indirect Image Registration with Large Diffeomorphic Deformations
Department of Mathematics, KTH–Royal Institute of Technology（数学系，皇家理工学院）；LSEC, ICMSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（数学和系统科学研究所，中国科学院）
Bayesian In Vivo Tracking of Synapses using Joint Poisson Deconvolution and Diffeomorphic Registration
Department of Biomedical Engineering, Johns Hopkins University（约翰霍普金斯大学生物医学工程系）；Department of Neuroscience, Johns Hopkins University（约翰霍普金斯大学神经科学系）；Kavli Neuroscience Discovery Institute, Johns Hopkins University（约翰霍普金斯大学Kavli神经科学发现研究所）；Data Science and AI Institute, Johns Hopkins University（约翰霍普金斯大学数据科学与人工智能研究所）；Department of Applied Mathematics and Statistics, Johns Hopkins University（约翰霍普金斯大学应用数学与统计学系）
Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration
School of Computer Science, University of Birmingham（伯明翰大学计算机学院）；Institute of Cardiovascular Sciences, University of Birmingham（伯明翰大学心血管科学研究所）；MRC London Institute of Medical Sciences, Imperial College London（帝国理工学院伦敦医学研究委员会医学科学研究所）；Institute of Cancer and Genomic Sciences, University of Birmingham（伯明翰大学癌症与基因组科学研究所）
Generative Aging of Brain Images with Diffeomorphic Registration
KTH Royal Institute of Technology（瑞典皇家理工学院）；Karolinska Institute（卡罗林斯卡学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；University Fernando Pessoa Canarias（费尔南多·佩索阿加那利群岛大学）；King’s College London（伦敦国王学院）
