We present an implementation of a new approach to diffeomorphic non-rigid registration of medical images. The method is based on optical flow and warps images via gradient flow with the standard $L^2$ inner product. To compute the transformation, we rely on accelerated optimisation on the manifold of diffeomorphisms. We achieve regularity properties of Sobolev gradient flows, which are expensive to compute, owing to a novel method of averaging the gradients in time rather than space. We successfully register brain MRI and challenging abdominal CT scans at speeds orders of magnitude faster than previous approaches. We make our code available in a public repository: https://github.com/dgrzech/fastreg
AdamFlow: Adam-based Wasserstein Gradient Flows for Surface Registration in Medical Imaging
Department of Brain Sciences, Imperial College London（帝国理工学院脑科学系）；Department of Computing, Imperial College London（帝国理工学院计算系）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）；Department of Computer Science, Columbia University（哥伦比亚大学计算机科学系）；Data Science Institute, Imperial College London（帝国理工学院数据科学研究所）
In this work, we propose a self-supervised learning method for affine image registration on 3D medical images. Unlike optimisation-based methods, our affine image registration network (AIRNet) is designed to directly estimate the transformation parameters between two input images without using any metric, which represents the quality of the registration, as the optimising function. But since it is costly to manually identify the transformation parameters between any two images, we leverage the abundance of cheap unlabelled data to generate a synthetic dataset for the training of the model. Add…
Universität zu Lübeck（吕贝克大学）；Institute of Medical Informatics（医学信息学研究所）
As in other areas of medical image analysis, e.g. semantic segmentation, deep learning is currently driving the development of new approaches for image registration. Multi-scale encoder-decoder network architectures achieve state-of-the-art accuracy on tasks such as intra-patient alignment of abdominal CT or brain MRI registration, especially when additional supervision, such as anatomical labels, is available. The success of these methods relies to a large extent on the outstanding ability of deep CNNs to extract descriptive visual features from the input images. In contrast to conventional m…
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）
Real-time registration of partially overlapping point clouds has emerging applications in cooperative perception for autonomous vehicles and multi-agent SLAM. The relative translation between point clouds in these applications is higher than in traditional SLAM and odometry applications, which challenges the identification of correspondences and a successful registration. In this paper, we propose a novel registration method for partially overlapping point clouds where correspondences are learned using an efficient point-wise feature encoder, and refined using a graph-based attention network. …
Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made difficult by the high calculation time needed to solve the optimisation problem. Deep learning overtook this limitation by taking advantage of GPU calculation and the learning process. However, many deep learning methods do not take into account desirable properties respected by classical algorithms. In this paper, we present MICS, a novel deep learning algorithm for medical imaging registration. As registration is an…
National University of Singapore（新加坡国立大学）；University of Cambridge（剑桥大学）
Indirect image registration is a promising technique to improve image reconstruction quality by providing a shape prior for the reconstruction task. In this paper, we propose a novel hybrid method that seeks to reconstruct high quality images from few measurements whilst requiring low computational cost. With this purpose, our framework intertwines indirect registration and reconstruction tasks is a single functional. It is based on two major novelties. Firstly, we introduce a model based on deep nets to solve the indirect registration problem, in which the inversion and registration mappings …
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…
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 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 a…
The University of Sydney（悉尼大学）；University of Technology Sydney（悉尼科技大学）
We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without correspondences. The advantage of the feature-metric projection error is robust to noise, outliers and density difference in contrast to the geometric projection error. Besides, minimising the feature-metric projection error does not need to search the correspondences so that the optimisation speed is fast. The principle behind the proposed method is that the feature difference is smallest if point clouds are aligned very w…
Correspondence-Free Multiview Point Cloud Registration via Depth-Guided Joint Optimisation
Robotics Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（机器人研究所，工程与信息技术学院，新南威尔士大学）；School of Informatics, The University of Edinburgh（信息学院，爱丁堡大学）
Eddeep: a deep-learning framework for fast eddy-current distortion correction in diffusion MRI
Hawkes Institute（霍克斯研究所）；University College London（伦敦大学学院）；Institut Pasteur（巴斯德研究所）；Université Paris Cité（巴黎西岱大学）；AINOSTICS ltd.（AINOSTICS有限公司）；Fudan University（复旦大学）；Université Libre de Bruxelles (ULB)（布鲁塞尔自由大学）；University of Liège (ULiège)（列日大学）
A useful application of event sensing is visual odometry, especially in settings that require high-temporal resolution. The state-of-the-art method of contrast maximisation recovers the motion from a batch of events by maximising the contrast of the image of warped events. However, the cost scales with image resolution and the temporal resolution can be limited by the need for large batch sizes to yield sufficient structure in the contrast image. In this work, we propose spatiotemporal registration as a compelling technique for event-based rotational motion estimation. We theoretcally justify …
Medical image segmentation is a relevant task as it serves as the first step for several diagnosis processes, thus it is indispensable in clinical usage. Whilst major success has been reported using supervised techniques, they assume a large and well-representative labelled set. This is a strong assumption in the medical domain where annotations are expensive, time-consuming, and inherent to human bias. To address this problem, unsupervised techniques have been proposed in the literature yet it is still an open problem due to the difficulty of learning any transformation pattern. In this work,…
International Prevention Research Institute（国际预防研究所）；Swansea University（斯旺西大学）；Diabetic Eye Screening Wales（威尔士糖尿病眼部筛查机构）；University of Strathclyde（思克莱德大学）
In this paper, a method is presented for superimposition (i.e. registration) of eye fundus images from persons with diabetes screened over many years for diabetic retinopathy. The method is fully automatic and robust to camera changes and colour variations across the images both in space and time. All the stages of the process are designed for longitudinal analysis of cohort public health databases where retinal examinations are made at approximately yearly intervals. The method relies on a model correcting two radial distortions and an affine transformation between pairs of images which is ro…
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…
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…
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.
Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN …
University of Edinburgh（爱丁堡大学）；Heriot-Watt University（赫瑞-瓦特大学）
Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsupervised image-to-image translation network that significantly improves the success of the co-registration using a conventional optimisation-based regression network, applicable to autofluorescence lifetime images at different emission wavelengths. A preliminary blind comparison …
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…
Imperial College London（帝国理工学院）；INRIA（法国国家信息与自动化研究所）；King’s College London（伦敦国王学院）；HeartFlow（HeartFlow公司）
Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis with applications such as: image rigid registration, anatomical standard plane detection, tracking and device/camera pose estimation. Deep learning methods often parameterise a pose with a representation that separates rotation and translation. As commonly available frameworks do not provide means to calculate loss on a manifold, regression is usually performed using the L2-norm independently on the rotation's and the translation's …
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
EchoScout GmbH；Institute of Medical Informatics（医学信息学研究所）；Institute of Applied Medical Informatics（应用医学信息学研究所）；Institute of Computational Neuroscience（计算神经科学研究所）；School of Artificial Intelligence and Robotics（人工智能与机器人学院）；Cornell University（康奈尔大学）；University of Electronic Science and Technology of China（电子科技大学）；Mechanical Engineering Department（机械工程系）；Harvard Medical School（哈佛医学院）；Technical University of Munich（慕尼黑技术大学）；Aalto University（阿德莱德大学）；Canon Medical Systems (China) Co., Ltd.（佳能医疗系统（中国）有限公司）；Smart Medical Imaging, Learning and Engineering (SMLE) Lab（智能医学影像、学习和工程实验室）；School of Information Science and Engineering（信息科学与工程学院）；Department of Imaging Physics（影像物理系）；Clinical Computational Medical Imaging Research（临床计算医学成像研究）；TUM Klinikum Rechts der Isar（慕尼黑工业大学医院）；University of California（加州大学）；AGH University of Krakow（克拉科夫应用科学大学）
Luxembourg Centre for Systems Biomedicine, University of Luxembourg（卢森堡大学系统生物医学中心）；Centre Hospitalier de Luxembourg（卢森堡中心医院）；Technical University of Munich (TUM)（慕尼黑工业大学）
We propose a combinatorial solution for the problem of non-rigidly matching a 3D shape to 3D image data. To this end, we model the shape as a triangular mesh and allow each triangle of this mesh to be rigidly transformed to achieve a suitable matching to the image. By penalising the distance and the relative rotation between neighbouring triangles our matching compromises between image and shape information. In this paper, we resolve two major challenges: Firstly, we address the resulting large and NP-hard combinatorial problem with a suitable graph-theoretic approach. Secondly, we propose an …
