Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. In recent years, some learning-based fully differentiable methods have produced beneficial outcomes while the process of feature extraction and gradient flow transmission still lack controllability and interpretability. To alleviate these problems, in this work, we propose a novel fully differentiable correlation-driven network using a dual-branch CNN-transformer encoder which enables the network to extract and separate low-frequency global features from high-frequency local features. A correlation-driven loss is further proposed for low-frequency feature and high-frequency feature decomposition based on embedded information. Besides, a training strategy that learns to approximate a convex-shape similarity function is applied in our work. We test our approach on a in-house datasetand show that it outperforms both existing fully differentiable learning-based registration approaches and the conventional optimization-based baseline.
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）
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.
In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion. Our method for fusion combines images from different spectral channels into a single fused image by different approaches for low and high frequency signals. A prerequisite of fusion is a stage of geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel do not yield reasonable results on images from different modalities. For that end, we introduce a new algorithm for multi-spectral …
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combined to generate target segmentation via label fusion schemes. Many conventional MAS methods employed the atlases from the same modality as the target image. However, the number of atlases with the same modality may be limited or even missing in many clinical applications. Besides, conventional MAS methods suffer from the computational burden of reg…
FusionRegister: Every Infrared and Visible Image Fusion Deserves Registration
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China（江南大学人工智能与计算机科学学院）；School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China（苏州科技大学电子与信息工程学院）
Deep Learning-based 2D/3D registration enables fast, robust, and accurate X-ray to CT image fusion when large annotated paired datasets are available for training. However, the need for paired CT volume and X-ray images with ground truth registration limits the applicability in interventional scenarios. An alternative is to use simulated X-ray projections from CT volumes, thus removing the need for paired annotated datasets. Deep Neural Networks trained exclusively on simulated X-ray projections can perform significantly worse on real X-ray images due to the domain gap. We propose a self-super…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；National Institutes of Health（美国国立卫生研究院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Robust and accurate alignment of multimodal medical images is a very challenging task, which however is very useful for many clinical applications. For example, magnetic resonance (MR) and transrectal ultrasound (TRUS) image registration is a critical component in MR-TRUS fusion guided prostate interventions. However, due to the huge difference between the image appearances and the large variation in image correspondence, MR-TRUS image registration is a very challenging problem. In this paper, an adversarial image registration (AIR) framework is proposed. By training two deep neural networks s…
Robust and Accurate Multi-view 2D/3D Image Registration with Differentiable X-ray Rendering and Dual Cross-view Constraints
Department of Information Science and Engineering, Shandong University（信息科学与工程学院，山东大学）；School of Control Science and Engineering, Shangdong University（控制科学与工程学院，山东大学）；Shenzhen Key Laboratory of Robotics Perception and Intelligence（机器人感知与智能实验室）；Dept. of Electronic and Electrical Engineering, Southern University of Science and Technology（电子与电气工程系，南方科技大学）；Dept. of Electronic Engineering, The Chinese University of Hong Kong（电子工程系，香港中文大学）
Bi-directional Self-Registration for Misaligned Infrared-Visible Image Fusion
College of Intelligence and Computing（智能与计算学院）；Tianjin University（天津大学）；School of Computer Science and Technology（计算机科学与技术学校）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Registration-Free Hybrid Learning Empowers Simple Multimodal Imaging System for High-quality Fusion Detection
School of Electronics and Information Engineering, Wuxi University（无锡大学电子与信息工程学院）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）
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）
Unsupervised Deformable Image Registration with Local-Global Attention and Image Decomposition
Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）；Department of Radiology, Peking University Third Hospital（北京大学第三医院放射科）；Department of Pediatrics, Peking University First Hospital（北京大学第一医院儿科）；Pediatric Epilepsy Center, Peking University First Hospital（北京大学第一医院儿童癫痫中心）；School of Biological Science and Medical Engineering, Southeast University（东南大学生物科学与医学工程学院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
Cross-Modality Feature Fusion Based on Structured State Space Duality for Multimodal Image Registration Network
Remote Sensing Image Processing and Fusion Group, School of Electronic Engineering, Xidian University（遥感图像处理与融合组，电子工程学院，西安电子科技大学）；National Key Laboratory of Radar Signal Processing, Xidian University（雷达信号处理国家级实验室，西安电子科技大学）
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）
In this paper, we propose a novel method for image fusion with a high-resolution panchromatic image and a low-resolution multispectral image at the same geographical location. The fusion is formulated as a convex optimization problem which minimizes a linear combination of a least-squares fitting term and a dynamic gradient sparsity regularizer. The former is to preserve accurate spectral information of the multispectral image, while the latter is to keep sharp edges of the high-resolution panchromatic image. We further propose to simultaneously register the two images during the fusing proces…
Fuzhou University（福州大学）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；King’s College London（伦敦国王学院）
Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consiste…
Element AI；Mila；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end…
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…
Johns Hopkins University（约翰斯·霍普金斯大学）；National Institute on Aging（国家老龄化研究所）；National Institute of Health（美国国立卫生研究院）
The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems. We argue that the latter task is challenging for traditional CNNs, limiting their performance in registration tasks. To tackle this problem, we first introduce Coordinate Translator, a differentiable module that identifies matched features between…
Zhejiang University（浙江大学）；Shandong Medical Imaging Research Institute（山东医学影像学研究所）；Cheeloo College of Medicine, Shandong University（山东大学齐鲁医学院）；Shanghai Jiao Tong University（上海交通大学）
Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion. Hence, stable and robust motion correction is necessary to reconstruct high-resolution 3D fetal brain volume for clinical diagnosis and quantitative analysis. However, the conventional registration-based correction has a limited capture range and is insufficient for detecting relatively large motions. Here, we present a novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM) correction of the multi-slice fetal brain MRI. It learns the sequent…
multimodars: A Rust-powered toolkit for multi-modality cardiac image fusion and registration
Department of Cardiology, Inselspital, Bern University Hospital, University of Bern（伯尔尼大学伯尔尼大学医院 Inselspital 心脏病学系）；Graduate School for Cellular and Biomedical Sciences, University of Bern（伯尔尼大学细胞与生物医学科学研究生院）
Yunnan Normal University（云南师范大学）；University of Nottingham Ningbo China（宁波诺丁汉大学）；Aba Teachers University（阿坝师范学院）
As an essential processing step before the fusing of infrared and visible images, the performance of image registration determines whether the two images can be fused at correct spatial position. In the actual scenario, the varied imaging devices may lead to a change in perspective or time gap between shots, making significant non-rigid spatial relationship in infrared and visible images. Even if a large number of feature points are matched, the registration accuracy may still be inadequate, affecting the result of image fusion and other vision tasks. To alleviate this problem, we propose a Se…
Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration between these datasets may fail to fully profit the potential of both sensors. In this regard, an optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when considering the context where data are collected at different times, from different platforms, under different acquisition configurations. This paper…
Shanghai University（上海大学）；University of Copenhagen（哥本哈根大学）
In this paper, we propose a new approach to deformable image registration that captures sliding motions. The large deformation diffeomorphic metric mapping (LDDMM) registration method faces challenges in representing sliding motion since it per construction generates smooth warps. To address this issue, we extend LDDMM by incorporating both zeroth- and first-order momenta with a non-differentiable kernel. This allows to represent both discontinuous deformation at switching boundaries and diffeomorphic deformation in homogeneous regions. We provide a mathematical analysis of the proposed deform…
Tsinghua University（清华大学）；Shenzhen International Graduate School（深圳国际研究生院）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根和妇女医院）
Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estima…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Philips Research North America（飞利浦北美研究院）；National Institutes of Health（美国国立卫生研究院）
Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images for guiding targeted prostate biopsy has significantly improved the biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image registration. However, it is very challenging to obtain a robust automatic MR-TRUS registration due to the large appearance difference between the two imaging modalities. The work presented in this paper aims to tackle this problem by addressing two challenges: (i) the definition of a suitable similarity metric and (ii) the determination of a suitable optimizatio…
Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy
College of Medicine and Biological Information Engineering（医学与生物信息工程学院）；Cancer Hospital of Dalian University of Technology（大连理工大学肿瘤医院）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）
The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, whic…
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…
Image registration is the process of transforming different sets of data into one coordinate system and is required for various remote sensing applications like change detection, image fusion, and other related areas. The effect of increased relief displacement, requirement of more control points, and increased data volume are the challenges associated with the registration of high resolution image data. The objective of this research work is to study the most efficient techniques and to investigate the extent of improvement achievable by enhancing them with Wavelet transform. The SIFT feature…
There is much current interest in using multi-sensor airborne remote sensing to monitor the structure and biodiversity of forests. This paper addresses the application of non-parametric image registration techniques to precisely align images obtained from multimodal imaging, which is critical for the successful identification of individual trees using object recognition approaches. Non-parametric image registration, in particular the technique of optimizing one objective function containing data fidelity and regularization terms, provides flexible algorithms for image registration. Using a sur…
Multi-Expert Learning Framework with the State Space Model for Optical and SAR Image Registration
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China（教育部智能感知与图像理解重点实验室）；Hangzhou Institute of Technology, Xidian University（西安电子科技大学杭州学院）；School of Computer Science, Xidian University（西安电子科技大学计算机学院）；Department of Automation, Tsinghua University（清华大学自动化系）
In this paper we propose a very efficient method to fuse the unregistered multi-focus microscopical images based on the speed-up robust features (SURF). Our method follows the pipeline of first registration and then fusion. However, instead of treating the registration and fusion as two completely independent stage, we propose to reuse the determinant of the approximate Hessian generated in SURF detection stage as the corresponding salient response for the final image fusion, thus it enables nearly cost-free saliency map generation. In addition, due to the adoption of SURF scale space represen…
Multi-Resolution SAR and Optical Remote Sensing Image Registration Methods: A Review, Datasets, and Future Perspectives
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA, Ministry of Natural Resources（自然资源部粤港澳自然资源数据协同应用技术创新中心）
An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion
University of Leeds（利兹大学）；Centre Hospitalier Universitaire de Clermont-Ferrand（克莱蒙费朗大学医院中心）；National University of Singapore (NUS)（新加坡国立大学）；National Center for Tumor Diseases (NCT/UCC Dresden)（德国国家肿瘤疾病中心（德累斯顿））；Fudan University（复旦大学）；University of Pennsylvania（宾夕法尼亚大学）
A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints
Cornell University（康奈尔大学）；Cornell Tech（康奈尔理工学院）；Weill Cornell Medical School（威尔康奈尔医学院）；Iterative Scopes（Iterative Scopes公司）；Massachusetts Institute of Technology（麻省理工学院）；Massachusetts General Hospital（麻省总医院）
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
Single-photon emission computed tomography (SPECT) is a widely applied imaging approach for diagnosis of coronary artery diseases. Attenuation maps (u-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve diagnostic accuracy of cardiac SPECT. However, SPECT and CT are obtained sequentially in clinical practice, which potentially induces misregistration between the two scans. Convolutional neural networks (CNN) are powerful tools for medical image registration. Previous CNN-based methods for cross-modality registration either directly concatenated t…
Multispectral and multimodal images are of important usage in the field of multi-source visual information fusion. Due to the alternation or movement of image devices, the acquired multispectral and multimodal images are usually misaligned, and hence image registration is pre-requisite. Different from the registration of common images, the registration of multispectral or multimodal images is a challenging problem due to the nonlinear variation of intensity and gradient. To cope with this challenge, we propose the phase congruency network (PCNet) to enhance the structure similarity of multispe…
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）
Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric constraints, e.g., topology-preserving, which are indispensable to generate plausible deformations. We design a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation in order to integrate advantages and …
Rensselaer Polytechnic Institute（伦斯勒理工学院）；National Institutes of Health（美国国立卫生研究院）；National Cancer Institute（美国国家癌症研究所）
Prostate cancer biopsy benefits from accurate fusion of transrectal ultrasound (TRUS) and magnetic resonance (MR) images. In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration. However, challenging applications and recent advances in computer vision suggest that CNNs are quite limited in its ability to understand spatial correspondence between features, a task in which the self-attention mechanism excels. This paper aims to develop a self-attention mechanism specifically for cross-modal image registrati…
Georgia Institute of Technology（佐治亚理工学院）；Massachusetts Institute of Technology（麻省理工学院）；National Institute of Mental Health（美国国家精神卫生研究所）；The Chinese University of Hong Kong（香港中文大学）
Image registration has been widely studied over the past several decades, with numerous applications in science, engineering and medicine. Most of the conventional mathematical models for large deformation image registration rely on prescribed landmarks, which usually require tedious manual labeling and are prone to error. In recent years, there has been a surge of interest in the use of machine learning for image registration. In this paper, we develop a novel method for large deformation image registration by a fusion of quasiconformal theory and convolutional neural network (CNN). More spec…
