Autograd-based software packages have recently renewed interest in image registration using homography and other geometric models by gradient descent and optimization, e.g., AirLab and DRMIME. In this work, we emphasize on using complex matrix exponential (CME) over real matrix exponential to compute transformation matrices. CME is theoretically more suitable and practically provides faster convergence as our experiments show. Further, we demonstrate that the use of an ordinary differential equation (ODE) as an optimizable dynamical system can adapt the transformation matrix more accurately to the multi-resolution Gaussian pyramid for image registration. Our experiments include four publicly available benchmark datasets, two of them 2D and the other two being 3D. Experiments demonstrate that our proposed method yields significantly better registration compared to a number of off-the-shelf, popular, state-of-the-art image registration toolboxes.
Deformable image registration (DIR), aiming to find spatial correspondence between images, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a novel, generic, and accurate diffeomorphic image registration framework that utilizes neural ordinary differential equations (NODEs). We model each voxel as a moving particle and consider the set of all voxels in a 3D image as a high-dimensional dynamical system whose trajectory determines the targeted deformation field. Our method leverages deep neural networks for their expressive power in modeling…
University of Alberta（阿尔伯塔大学）；Hospital Aleman（阿勒曼医院）
In this work, we present a novel unsupervised image registration algorithm. It is differentiable end-to-end and can be used for both multi-modal and mono-modal registration. This is done using mutual information (MI) as a metric. The novelty here is that rather than using traditional ways of approximating MI, we use a neural estimator called MINE and supplement it with matrix exponential for transformation matrix computation. This leads to improved results as compared to the standard algorithms available out-of-the-box in state-of-the-art image registration toolboxes.
We present a postprocessing layer for deformable image registration to make a registration field more diffeomorphic by encouraging Jacobians of the transformation to be positive. Diffeomorphic image registration is important for medical imaging studies because of the properties like invertibility, smoothness of the transformation, and topology preservation/non-folding of the grid. Violation of these properties can lead to destruction of the neighbourhood and the connectivity of anatomical structures during image registration. Most of the recent deep learning methods do not explicitly address t…
Queensland University of Technology（昆士兰科技大学）；CSIRO（联邦科学与工业研究组织）
The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts. Most of the learning-based registration approaches considers this task as a one directional problem. As a result, only correspondence from the moving image to the target image is considered. However, in some medical procedures bidirectional registration is required to be performed. Unlike other learning-based registration, we propose a registration framework with invers…
EDFFDNet: Towards Accurate and Efficient Unsupervised Multi-Grid Image Registration
Ningbo Global Innovation Center, Zhejiang University（浙江大学宁波全球创新中心）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；NingboTech University（宁波科技大学）；Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology, Hangzhou, China（浙江大数据与未来电子商务技术重点实验室）
University of Alberta（阿尔伯塔大学）；Servier Virtual Cardiac Centre（施维雅虚拟心脏中心）；Mazankowski Alberta Heart Institute（马赞科夫斯基阿尔伯塔心脏研究所）
Diffeomorphic deformable image registration is one of the crucial tasks in medical image analysis, which aims to find a unique transformation while preserving the topology and invertibility of the transformation. Deep convolutional neural networks (CNNs) have yielded well-suited approaches for image registration by learning the transformation priors from a large dataset. The improvement in the performance of these methods is related to their ability to learn information from several sample medical images that are difficult to obtain and bias the framework to the specific domain of data. In thi…
This work addresses the problem of non-rigid registration of 3D scans, which is at the core of shape modeling techniques. Firstly, we propose a new kernel based on geodesic distances for the Gaussian Process Morphable Models (GPMMs) framework. The use of geodesic distances into the kernel makes it more adapted to the topological and geometric characteristics of the surface and leads to more realistic deformations around holes and curved areas. Since the kernel possesses hyperparameters we have optimized them for the task of face registration on the FaceWarehouse dataset. We show that the Geode…
FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；KRH Klinikum Nordstadt（KRH北城医院）；Universität Klinikum Erlangen（埃尔朗根大学医院）
For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity. FDR algorithms estimate a dense displacement field by interpolating a sparse field, which is given by the established correspondence between selected features. In this paper, we consider the deformation field as a Gaussian Process (GP), whereas the selected features are regarded as prior information on the valid deformations. Using GP, we are able to…
Registration of 3D Point Sets Using Exponential-based Similarity Matrix
Advanced Robotics and Automation (ARA) Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno（高级机器人与自动化实验室，计算机科学与工程系，内华达大学雷诺分校）
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of motion. Additional alignment cues (point-based or geometric features, and other boundary co…
OCA: ODE-Driven Cross-Attention for Image-to-Point-Cloud Registration
Huazhong University of Science and Technology（华中科技大学）；Northwestern Polytechnical University（西北工业大学）；Huazhong Agricultural University（华中农业大学）；Delft University of Technology（代尔夫特理工大学）
Institut Mines Télécom Atlantique（法国大西洋矿业电信学院）；INSERM（法国国家健康与医学研究院）；UBL（法国布列塔尼大学联盟）；Aix Marseille Univ（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；University of Western Brittany（西布列塔尼大学）；Military Teaching Hospital Clermont-Tonnerre（克莱蒙-通讷尔军事教学医院）；CHRU of Brest（布雷斯特大学医疗中心）
The log Euclidean polyrigid registration framework provides a way to smoothly estimate and interpolate poly-rigid/affine transformations for which the invertibility is guaranteed. This powerful and flexible mathematical framework is currently being used to track the human joint dynamics by first imposing bone rigidity constraints in order to synthetize the spatio-temporal joint deformations later. However, since no closed-form exists, then a computationally expensive integration of ordinary differential equations (ODEs) is required to perform image registration using this framework. To tackle …
Fudan University（复旦大学）；Technical University of Munich（慕尼黑工业大学）
The rigid registration of two 3D point sets is a fundamental problem in computer vision. The current trend is to solve this problem globally using the BnB optimization framework. However, the existing global methods are slow for two main reasons: the computational complexity of BnB is exponential to the problem dimensionality (which is six for 3D rigid registration), and the bound evaluation used in BnB is inefficient. In this paper, we propose two techniques to address these problems. First, we introduce the idea of translation invariant vectors, which allows us to decompose the search of a 6…
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）
Beihang University（北京航空航天大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Peng Cheng Laboratory（鹏城实验室）
Point set registration is an essential step in many computer vision applications, such as 3D reconstruction and SLAM. Although there exist many registration algorithms for different purposes, however, this topic is still challenging due to the increasing complexity of various real-world scenarios, such as heavy noise and outlier contamination. In this paper, we propose a novel probabilistic generative method to simultaneously align multiple point sets based on the heavy-tailed Laplacian distribution. The proposed method assumes each data point is generated by a Laplacian Mixture Model (LMM), w…
Facebook Artificial Intelligence（脸书人工智能）；University of Southern California（南加州大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Deformable registration of magnetic resonance images between patients with brain tumors and healthy subjects has been an important tool to specify tumor geometry through location alignment and facilitate pathological analysis. Since tumor region does not match with any ordinary brain tissue, it has been difficult to deformably register a patients brain to a normal one. Many patient images are associated with irregularly distributed lesions, resulting in further distortion of normal tissue structures and complicating registration's similarity measure. In this work, we follow a multi-step contex…
Institute of Parallel and Distributed Systems, University of Stuttgart（斯图加特大学并行与分布式系统研究所）
Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost the registration performance in both accuracy and runtime, we propose a fast convolutional neural network. Specially, to efficiently utilize the memory resources and enlarge the model capacity, we adopt additive forwarding instead of channel concatenation and deepen the network in each enc…
