This paper investigates the use of evolutionary optimisation techniques to register a template with a scene image. An error function is created to measure the correspondence of the template to the image. The problem presented here is to optimise the horizontal, vertical and scaling parameters that register the template with the scene. The Genetic Algorithm, Simulated Annealing and Particle Swarm Optimisations are compared to a Nelder-Mead Simplex optimisation with starting points chosen in a pre-processing stage. The paper investigates the precision and accuracy of each method and shows that all four methods perform favourably for image registration. SA is the most precise, GA is the most accurate. PSO is a good mix of both and the Simplex method returns local minima the most. A pre-processing stage should be investigated for the evolutionary methods in order to improve performance. Discrete versions of the optimisation methods should be investigated to further improve computational performance.
Institute of Medical Informatics, Universität zu Lübeck（吕贝克大学医学信息学研究所）
Current approaches for deformable medical image registration often struggle to fulfill all of the following criteria: versatile applicability, small computation or training times, and the being able to estimate large deformations. Furthermore, end-to-end networks for supervised training of registration often become overly complex and difficult to train. For the Learn2Reg2021 challenge, we aim to address these issues by decoupling feature learning and geometric alignment. First, we introduce a new very fast and accurate optimisation method. By using discretised displacements and a coupled conve…
University of Copenhagen（哥本哈根大学）；Biomediq A/S；Lundbeck（灵北）
Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability. While building models for template estimation, variability in sites and image acquisition protocols need to be accounted for. To account for such variability, we propose a generative template estimation model that makes simultaneous inference of both bias fields in individual images, deformations for image registration, and variance hyperparameters. In contrast, existing maximum a posterori based methods ne…
Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis. Current conventional and deep network-based methods for template construction use only regularized registration objectives and often yield templates with blurry and/or anatomically implausible appearance, confounding downstream biomedical interpretation. We reformulate deformable registration and conditional template estimation as an adversarial game wherein we encourage realism in the moved templates with a generative adversarial registration framework conditioned on flexible i…
The majority of current research in deep learning based image registration addresses inter-patient brain registration with moderate deformation magnitudes. The recent Learn2Reg medical registration benchmark has demonstrated that single-scale U-Net architectures, such as VoxelMorph that directly employ a spatial transformer loss, often do not generalise well beyond the cranial vault and fall short of state-of-the-art performance for abdominal or intra-patient lung registration. Here, we propose two straightforward steps that greatly reduce this gap in accuracy. First, we employ keypoint self-s…
This paper introduces a class of mixed-effects models for joint modeling of spatially correlated intensity variation and warping variation in 2D images. Spatially correlated intensity variation and warp variation are modeled as random effects, resulting in a nonlinear mixed-effects model that enables simultaneous estimation of template and model parameters by optimization of the likelihood function. We propose an algorithm for fitting the model which alternates estimation of variance parameters and image registration. This approach avoids the potential estimation bias in the template estimate …
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）
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）
Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate, they typically carry a significant computational burden. Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods. In this paper, we attempt to reduce this difference in speed whilst retaining the performance advantage of iterative approaches in DiffIR. We firs…
Wellcome/EPSRC Centre for Surgical and Interventional Sciences, University College London（伦敦大学学院威康/EPSRC外科与介入科学中心）；Centre for Medical Image Computing, University College London（伦敦大学学院医学图像计算中心）；InstaDeep（InstaDeep公司）；Department of Surgical and Interventional Engineering, King’s College London（伦敦国王学院外科与介入工程系）
This document outlines a tutorial to get started with medical image registration using the open-source package DeepReg. The basic concepts of medical image registration are discussed, linking classical methods to newer methods using deep learning. Two iterative, classical algorithms using optimisation and one learning-based algorithm using deep learning are coded step-by-step using DeepReg utilities, all with real, open-accessible, medical data.
Chinese Academy of Sciences（中国科学院）；KTH – Royal Institute of Technology（瑞典皇家理工学院）
This article adapts the framework of metamorphosis to solve inverse problems in imaging that includes joint reconstruction and image registration. The deformations in question have two components, one that is a geometric deformation moving intensities and the other a deformation of intensity values itself, which, e.g., allows for appearance of a new structure. The idea developed here is to reconstruct an image from noisy and indirect observations by registering, via metamorphosis, a template to the observed data. Unlike a registration with only geometrical changes, this framework gives good re…
Institute of Medical Informatics, Universität zu Lübeck（吕贝克大学医学信息学研究所）
Nonlinear image registration continues to be a fundamentally important tool in medical image analysis. Diagnostic tasks, image-guided surgery and radiotherapy as well as motion analysis all rely heavily on accurate intra-patient alignment. Furthermore, inter-patient registration enables atlas-based segmentation or landmark localisation and shape analysis. When labelled scans are scarce and anatomical differences large, conventional registration has often remained superior to deep learning methods that have so far mainly dealt with relatively small or low-complexity deformations. We address thi…
The recent application of deep learning in various areas of medical image analysis has brought excellent performance gains. In particular, technologies based on deep learning in medical image registration can outperform traditional optimisation-based registration algorithms both in registration time and accuracy. However, the U-net based architectures used in most of the image registration frameworks downscale the data, which removes global information and affects the deformation. In this paper, we present a densely connected convolutional architecture for deformable image registration. Our pr…
Institute for Computational Engineering and Sciences, University of Texas at Austin（德克萨斯大学奥斯汀分校计算工程与科学研究所）；Department of Mathematics and Computer Science, Emory University（埃默里大学数学与计算机科学系）
We present an efficient solver for diffeomorphic image registration problems in the framework of Large Deformations Diffeomorphic Metric Mappings (LDDMM). We use an optimal control formulation, in which the velocity field of a hyperbolic PDE needs to be found such that the distance between the final state of the system (the transformed/transported template image) and the observation (the reference image) is minimized. Our solver supports both stationary and non-stationary (i.e., transient or time-dependent) velocity fields. As transformation models, we consider both the transport equation (ass…
Southwest Jiaotong University（西南交通大学）；University of Trento（特伦托大学）；Purdue University（普渡大学）
Automatic registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR) is a challenging task due to the significant non-linear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the Histogram of Orientated Phase Congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first…
Southwest Jiaotong University（西南交通大学）；Beijing Institute of Technology（北京理工学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）
Co-registration of multimodal remote sensing images is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions (e.g., scale and rotation changes) between these images. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. First, to address severe NRD, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed, which combines the first- and second-order gradient information by using the steerable filters with a multi-…
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）
Southwest Jiaotong University（西南交通大学）；University of Trento（特伦托大学）；Purdue University（普渡大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
While image registration has been studied in remote sensing community for decades, registering multimodal data [e.g., optical, LiDAR, SAR, and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration. In the proposed framework, a local descriptor, such as Histogram of Oriented Gradient (HOG), Local Self Similarity (LSS), or Speeded-Up Robust Feature (SURF), is first extracted at each pixel to form a pixel…
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）
WarpPINN: Cine-MR image registration with physics-informed neural networks
University of Bath（巴斯大学）；Pontificia Universidad Católica de Valparaíso（瓦尔帕莱索天主大学）；Millennium Institute for Intelligent Healthcare Engineering（千年智能医疗工程研究所）；Pontificia Universidad Católica de Chile（智利天主大学）
