Probabilistic image registration methods estimate the posterior distribution of transformation. The conventional way of interpreting the transformation posterior is to use the mode as the most likely transformation and assign its corresponding intensity to the registered voxel. Meanwhile, summary statistics of the posterior are employed to evaluate the registration uncertainty, that is the trustworthiness of the registered image. Despite the wide acceptance, this convention has never been justified. In this paper, based on illustrative examples, we question the correctness and usefulness of conventional methods. In order to faithfully translate the transformation posterior, we propose to encode the variability of values into a novel data type called ensemble fields. Ensemble fields can serve as a complement to the registered image and a foundation for developing advanced methods to characterize the uncertainty in registration-based tasks. We demonstrate the potential of ensemble fields by pilot examples
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）
This paper presents a novel mathematical framework for representing uncertainty in large deformation diffeomorphic image registration. The Bayesian posterior distribution over the deformations aligning a moving and a fixed image is approximated via a variational formulation. A stochastic differential equation (SDE) modeling the deformations as the evolution of a time-varying velocity field leads to a prior density over deformations in the form of a Gaussian process. This permits estimating the full posterior distribution in order to represent uncertainty, in contrast to methods in which the po…
Technion – Israel Institute of Technology（以色列理工学院）
Unsupervised deep-learning (DL) models were recently proposed for deformable image registration tasks. In such models, a neural-network is trained to predict the best deformation field by minimizing some dissimilarity function between the moving and the target images. After training on a dataset without reference deformation fields available, such a model can be used to rapidly predict the deformation field between newly seen moving and target images. Currently, the training process effectively provides a point-estimate of the network weights rather than characterizing their entire posterior d…
Imperial College London（帝国理工学院）；Technische Universität München（慕尼黑工业大学）；Sharif University of Technology（谢里夫理工大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backpropagation and the variational inference by backpropagation frameworks, in order to efficiently draw samples from the posterior distribution of transformation parameters. To address the modelling issues, we formulat…
University of Southern California（南加州大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP)…
We propose to view non-rigid surface registration as a probabilistic inference problem. Given a target surface, we estimate the posterior distribution of surface registrations. We demonstrate how the posterior distribution can be used to build shape models that generalize better and show how to visualize the uncertainty in the established correspondence. Furthermore, in a reconstruction task, we show how to estimate the posterior distribution of missing data without assuming a fixed point-to-point correspondence. We introduce the closest-point proposal for the Metropolis-Hastings algorithm. Ou…
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）
University of Science and Technology Houari Bouemadiene USTHB（胡阿里·布迈丁科学技术大学（USTHB））；Research Center on Scientific and Technical Information, CERIST（科学技术信息研究中心（CERIST））；Specialized Hospital Bounaama Djilali, CHU Douera（杜埃拉大学医院布纳马·吉拉利专科医院）；Saad Dahleb University（萨阿德·达赫莱布大学）；Center of Development of Advanced Technologies, CDTA（先进技术发展中心（CDTA））
Children diagnosed with a scoliosis pathology are exposed during their follow up to ionic radiations in each X-rays diagnosis. This exposure can have negative effects on the patient's health and cause diseases in the adult age. In order to reduce X-rays scanning, recent systems provide diagnosis of scoliosis patients using solely RGB images. The output of such systems is a set of augmented images and scoliosis related angles. These angles, however, confuse the physicians due to their large number. Moreover, the lack of X-rays scans makes it impossible for the physician to compare RGB and X-ray…
Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
Xi’an University of Posts and Telecommunications（西安邮电大学）；Nanjing Medical University（南京医科大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；The First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）
Technion – Israel Institute of Technology（以色列理工学院）
Quantification of uncertainty in deep-neural-networks (DNN) based image registration algorithms plays a critical role in the deployment of image registration algorithms for clinical applications such as surgical planning, intraoperative guidance, and longitudinal monitoring of disease progression or treatment efficacy as well as in research-oriented processing pipelines. Currently available approaches for uncertainty estimation in DNN-based image registration algorithms may result in sub-optimal clinical decision making due to potentially inaccurate estimation of the uncertainty of the registr…
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data. To address these issues, we propose UDPReg, an unsupervised deep probabilistic registration framework for point clouds with partial overlaps. Specifically, we first adopt a network to learn posterior probability distributions of Gaussian mixture models (GMMs) from point clouds. To handle partial point cloud registration, we apply the Sinkhorn algorithm to predict the distribution-level correspondences under the constraint of the mixing weights of GMMs. To enable unsupervised learning, we design …
Fudan University（复旦大学）；University of Washington（华盛顿大学）
Multimodal groupwise registration aligns internal structures in a group of medical images. Current approaches to this problem involve developing similarity measures over the joint intensity profile of all images, which may be computationally prohibitive for large image groups and unstable under various conditions. To tackle these issues, we propose BInGo, a general unsupervised hierarchical Bayesian framework based on deep learning, to learn intrinsic structural representations to measure the similarity of multimodal images. Particularly, a variational auto-encoder with a novel posterior is pr…
DINE: Distance Is Not Enough -- Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration
Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University（曼海姆医学智能系统研究所（MIISM），海德堡大学曼海姆医学院）；Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School（布莱根妇女医院放射肿瘤学系，达纳-法伯癌症研究所，哈佛医学院）；Department of Otorhinolaryngology, Head and Neck Surgery, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University（海德堡大学曼海姆医学院曼海姆大学医学中心耳鼻咽喉头颈外科）；Department of Otolaryngology, Head and Neck Surgery, Campus Klinikum Bielefeld Mitte, University Hospital OWL of Bielefeld University（比勒费尔德大学OWL大学医院比勒费尔德市中心校区耳鼻咽喉头颈外科）；Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University（海德堡大学跨学科科学计算中心（IWR））；Central Institute for Computer Engineering (ZITI), Heidelberg University（海德堡大学中央计算机工程研究所（ZITI））；CZS Heidelberg Center for Model-Based AI, Heidelberg University（海德堡大学CZS基于模型的人工智能中心）
Silhouette-to-Contour Registration: Aligning Intraoral Scan Models with Cephalometric Radiographs
School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, China（人工智能与先进计算学院，西安交通大学利物浦大学，中国）；School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, United Kingdom（电子工程、电子科学与计算机科学学院，利物浦大学，英国）；School of Advanced Technology, Xi'an Jiaotong-Liverpool University, China（先进技术学院，西安交通大学利物浦大学，中国）；School of Physical Sciences, University of Liverpool, Liverpool, United Kingdom（物理科学学院，利物浦大学，英国）；School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, China（数学与物理学院，西安交通大学利物浦大学，中国）；Institute of Artificial Intelligence Innovation, Zhejiang University of Technology, China（人工智能创新研究院，浙江工业大学，中国）
The present paper reviews the classical problem of free-form curve registration and applies it to an efficient RGBD visual odometry system called Canny-VO, as it efficiently tracks all Canny edge features extracted from the images. Two replacements for the distance transformation commonly used in edge registration are proposed: Approximate Nearest Neighbour Fields and Oriented Nearest Neighbour Fields. 3D2D edge alignment benefits from these alternative formulations in terms of both efficiency and accuracy. It removes the need for the more computationally demanding paradigms of datato-model re…
Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Aalto University（阿尔托大学）；Copenhagen University Hospital（哥本哈根大学医院）；University College London（伦敦大学学院）；Massachusetts Institute of Technology（麻省理工学院）
Submillimeter-Accurate 3D Lumbar Spine Reconstruction from Biplanar X-Ray Images: Incorporating a Multi-Task Network and Landmark-Weighted Loss
School of Biomedical Engineering & Med-X Research Institute, Shanghai Jiao Tong University（上海交通大学生物医学工程学院与Med-X研究院）；Engineering Research Center of Digital Medicine and Clinical Translation, Ministry of Education（教育部数字医学临床转化工程研究中心）；Department of Spine Surgery, Tongji Hospital, School of Medicine, Tongji University（同济大学医学院附属同济医院脊柱外科）；TAOiMAGE Medical Technologies Corporation（TAOiMAGE医疗科技有限公司）；School of Mechanical Engineering, Shanghai Jiao Tong University（上海交通大学机械工程学院）
