BrainMorph: A Foundational Keypoint Model for Robust and Flexible Brain MRI Registration
Department of Radiology, Weill Cornell Medicine, New York City, NY, USA（韦尔·科恩医学中心放射科）；School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA（康奈尔大学电气与计算机工程学院）；Computer Science and Artificial Intelligence Lab at the Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院计算机科学与人工智能实验室）
Alan Q. Wang, Rachit Saluja, Heejong Kim, Xinzi He, Adrian Dalca, Mert R. Sabuncu
We present a keypoint-based foundation model for general purpose brain MRI registration, based on the recently-proposed KeyMorph framework. Our model, called BrainMorph, serves as a tool that supports multi-modal, pairwise, and scalable groupwise registration. BrainMorph is trained on a massive dataset of over 100,000 3D volumes, skull-stripped and non-skull-stripped, from nearly 16,000 unique healthy and diseased subjects. BrainMorph is robust to large misalignments, interpretable via interrogating automatically-extracted keypoints, and enables rapid and controllable generation of many plausible transformations with different alignment types and different degrees of nonlinearity at test-time. We demonstrate the superiority of BrainMorph in solving 3D rigid, affine, and nonlinear registration on a variety of multi-modal brain MRI scans of healthy and diseased subjects, in both the pairwise and groupwise setting. In particular, we show registration accuracy and speeds that surpass many classical and learning-based methods, especially in the context of large initial misalignments and large group settings. All code and models are available at https://github.com/alanqrwang/brainmorph.
Institute of Medical Informatics, University of Lübeck（吕贝克大学医学信息学研究所）
Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep neural networks generally require plenty of labeled training data and are vulnerable to domain shifts between training and test data. While typical intensity shifts can be mitigated by keypoint-based registration, these methods still suffer from geometric domain shifts, for instance, due to different fields of view. As a remedy, in this work, we present a novel approach to geometric domain adaptation for image regis…
Deformable registration continues to be one of the key challenges in medical image analysis. While iconic registration methods have started to benefit from the recent advances in medical deep learning, the same does not yet apply for the registration of point sets, e.g. registration based on surfaces, keypoints or landmarks. This is mainly due to the restriction of the convolution operator in modern CNNs to densely gridded input. However, with the newly developed methods from the field of geometric deep learning suitable tools are now emerging, which enable powerful analysis of medical data on…
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
Radboud University Medical Centre（拉德堡德大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Macao Polytechnic University（澳门理工大学）；Maastricht University Medical Centre（马斯特里赫特大学医学中心）
Renmin University of China（中国人民大学）；Visionary Intelligence Ltd（远见智能有限公司）；Tongren Hospital（同仁医院）
For retinal image matching (RIM), we propose SuperRetina, the first end-to-end method with jointly trainable keypoint detector and descriptor. SuperRetina is trained in a novel semi-supervised manner. A small set of (nearly 100) images are incompletely labeled and used to supervise the network to detect keypoints on the vascular tree. To attack the incompleteness of manual labeling, we propose Progressive Keypoint Expansion to enrich the keypoint labels at each training epoch. By utilizing a keypoint-based improved triplet loss as its description loss, SuperRetina produces highly discriminativ…
Unsupervised training of keypoint-agnostic descriptors for flexible retinal image registration
Grupo VARPA, Instituto de Investigacion Biomédica de A Coruña (INIBIC), Universidade da Coruña（VARPA组，阿维拉生物医学研究所（INIBIC），科鲁纳大学）；Departamento de Ciencias de la Computación y Tecnologías de la Información, Universidade da Coruña（计算机科学与信息技术部门，科鲁纳大学）
In ophthalmological imaging, multiple imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography, are often involved to make a diagnosis of retinal disease. Multi-modal retinal registration techniques can assist ophthalmologists by providing a pixel-based comparison of aligned vessel structures in images from different modalities or acquisition times. To this end, we propose an end-to-end trainable deep learning method for multi-modal retinal image registration. Our method extracts convolutional features from the vessel struc…
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）
University of Waterloo（滑铁卢大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）
We present Recurrence with Correlation Network (RWCNet), a medical image registration network with multi-scale features and a cost volume layer. We demonstrate that these architectural features improve medical image registration accuracy in two image registration datasets prepared for the MICCAI 2022 Learn2Reg Workshop Challenge. On the large-displacement National Lung Screening Test (NLST) dataset, RWCNet is able to achieve a total registration error (TRE) of 2.11mm between corresponding keypoints without instance fine-tuning. On the OASIS brain MRI dataset, RWCNet is able to achieve an avera…
A Novel Streamline-based diffusion MRI Tractography Registration Method with Probabilistic Keypoint Detection
University of Electronic Science and Technology of China（电子科技大学）；Nanjing University of Science and Technology（南京理工大学）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根和妇女医院）
Fraunhofer MEVIS（弗劳恩霍夫医学图像计算研究所（MEVIS））；University of Lübeck（吕贝克大学）；Anthropics Technology Ltd.（Anthropics科技有限公司）
We present a novel algorithm for the registration of pulmonary CT scans. Our method is designed for large respiratory motion by integrating sparse keypoint correspondences into a dense continuous optimization framework. The detection of keypoint correspondences enables robustness against large deformations by jointly optimizing over a large number of potential discrete displacements, whereas the dense continuous registration achieves subvoxel alignment with smooth transformations. Both steps are driven by the same normalized gradient fields data term. We employ curvature regularization and a v…
A 3D Cross-modal Keypoint Descriptor for MR-US Matching and Registration
Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Inria（法国国家信息与自动化研究所）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière（巴黎大脑研究所 - ICM, 法国国家科学研究中心, 法国国家健康与医学研究院, 巴黎公立医院集团, 皮提耶-萨勒佩特里医院）；Technical University of Munich（慕尼黑工业大学）
Tongji University（同济大学）；Technische Universität München（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Westwell lab（西井实验室）
Point cloud registration is a fundamental problem in 3D computer vision. Outdoor LiDAR point clouds are typically large-scale and complexly distributed, which makes the registration challenging. In this paper, we propose an efficient hierarchical network named HRegNet for large-scale outdoor LiDAR point cloud registration. Instead of using all points in the point clouds, HRegNet performs registration on hierarchically extracted keypoints and descriptors. The overall framework combines the reliable features in deeper layer and the precise position information in shallower layers to achieve robu…
University of Pennsylvania（宾夕法尼亚大学）；University of Minnesota（明尼苏达大学）
This paper presents a semi-supervised learning framework to train a keypoint detector using multiview image streams given the limited labeled data (typically $<$4\%). We leverage the complementary relationship between multiview geometry and visual tracking to provide three types of supervisionary signals to utilize the unlabeled data: (1) keypoint detection in one view can be supervised by other views via the epipolar geometry; (2) a keypoint moves smoothly over time where its optical flow can be used to temporally supervise consecutive image frames to each other; (3) visible keypoint in one v…
We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformat…
Université de Lyon（里昂大学）；INSA Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣艾蒂安让·莫内大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；Hospices Civils de Lyon（里昂民用医院集团）
We present a novel algorithm for Fast Registration Of image Groups (FROG), applied to large 3D image groups. Our approach extracts 3D SURF keypoints from images, computes matched pairs of keypoints and registers the group by minimizing pair distances in a hubless way i.e. without computing any central mean image. Using keypoints significantly reduces the problem complexity compared to voxel-based approaches, and enables us to provide an in-core global optimization, similar to the Bundle Adjustment for 3D reconstruction. As we aim to register images of different patients, the matching step yiel…
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…
Implicit Deformable Medical Image Registration with Learnable Kernels
Department of Computer Science Interactive Graphics and Simulation Group (IGS), University of Innsbruck, Technikerstraße 21 Innsbruck, Austria（因计算机科学系交互式图形与模拟组（IGS），因斯布鲁克大学）；Interventional Oncology-Microinvasive Therapy (SIP), Department of Radiology, Medical University Innsbruck, Innsbruck, Austria（介入肿瘤学-微侵袭疗法（SIP），放射学系，因斯布鲁克医学院）
Shanghai Jiao Tong University（上海交通大学）；Qing Yuan Research Institute（清源研究院）；Shanghai Qi Zhi Institute（上海期智研究院）
Keypoint detection is an essential component for the object registration and alignment. In this work, we reckon keypoint detection as information compression, and force the model to distill out irrelevant points of an object. Based on this, we propose UKPGAN, a general self-supervised 3D keypoint detector where keypoints are detected so that they could reconstruct the original object shape. Two modules: GAN-based keypoint sparsity control and salient information distillation modules are proposed to locate those important keypoints. Extensive experiments show that our keypoints align well with …
Pattern Recognition Lab, FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学模式识别实验室）；Department of Ophthalmology, Universitätsklinikum Erlangen（埃尔朗根大学医院眼科）
Ophthalmological imaging utilizes different imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography. Multiple images with different modalities or acquisition times are often analyzed for the diagnosis of retinal diseases. Automatically aligning the vessel structures in the images by means of multi-modal registration can support the ophthalmologists in their work. Our method uses a convolutional neural network to extract features of the vessel structure in multi-modal retinal images. We jointly train a keypoint detection an…
Unsupervised Deep Learning-based Keypoint Localization Estimating Descriptor Matching Performance
Grupo VARPA（VARPA组）；Instituto de Investigacion Biomédica de A Coruña (INIBIC)（阿罗纳生物医学研究所（INIBIC））；Universidade da Coruña（科鲁纳大学）；Departamento de Ciencias de la Computación y Tecnologías de la Información（计算机科学与信息技术部门）
Technische Universität München（慕尼黑工业大学）；Google（谷歌）
3D Point clouds are a rich source of information that enjoy growing popularity in the vision community. However, due to the sparsity of their representation, learning models based on large point clouds is still a challenge. In this work, we introduce Graphite, a GRAPH-Induced feaTure Extraction pipeline, a simple yet powerful feature transform and keypoint detector. Graphite enables intensive down-sampling of point clouds with keypoint detection accompanied by a descriptor. We construct a generic graph-based learning scheme to describe point cloud regions and extract salient points. To this en…
Registration by Regression (RbR): a framework for interpretable and flexible atlas registration
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Danish Research Centre for Magnetic Resonance（丹麦磁共振研究中心）；Copenhagen University Hospital（哥本哈根大学医院）；Centre for Medical Image Computing（医学图像计算中心）；University College London（伦敦大学学院）；Computer Science and AI Laboratory（计算机科学与人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）
Istituto Italiano di Tecnologia（意大利理工学院）；Politecnico di Milano（米兰理工大学）；University College London（伦敦大学学院）；University College London Hospital（伦敦大学学院医院）；University Hospital Leuven（鲁汶大学医院）；Istituto "Giannina Gaslini"（詹尼娜·加斯利尼研究所）；Scuola Superiore Sant’Anna（圣安娜高等学校）
In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. In the current practice, TTTS is treated surgically by closing abnormal anastomoses using laser ablation. This surgery is minimally invasive and relies on fetoscopy. Limited field of view makes anastomosis identification a challenging task for the surgeon. To tackle this challenge, we propose a learning-based framework for in-vivo fetoscopy frame registration for field-of-view expansion. The novelties of this framework relies on a learn…
École Polytechnique de Montréal（蒙特利尔高等理工学院）；École de technologie supérieure（高等工程技术学院）
In this paper, an automatic method is proposed to perform image registration in visible and infrared pair of video sequences for multiple targets. In multimodal image analysis like image fusion systems, color and IR sensors are placed close to each other and capture a same scene simultaneously, but the videos are not properly aligned by default because of different fields of view, image capturing information, working principle and other camera specifications. Because the scenes are usually not planar, alignment needs to be performed continuously by extracting relevant common information. In th…
Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
LASIGE, Faculdade de Ciências da Universidade de Lisboa（里斯本大学科学学院LASIGE中心）；CRUK Cambridge Centre, University of Cambridge（剑桥大学CRUK剑桥中心）；Department of Oncology, University of Cambridge（剑桥大学肿瘤学系）；Intelligent System for Medicine Laboratory (ISML), School of Mechanical Engineering, The University of Western Australia（西澳大学机械工程学院医学智能系统实验室）；MIND Team, Inria Saclay, Université Paris-Saclay, Palaiseau, France（法国巴黎萨克利大学MIND团队）；Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière（索邦大学巴黎脑研究所-ICM）
An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers. Current 3D keypoint techniques are much less accurate than their 2D counterparts, thus they tend to produce extremely high outlier rates. A large number of putative correspondences must thus be extracted to ensure that sufficient good correspondences are available. Both factors (high outlier rates, large data sizes) however cause existing robust t…
The University of Adelaide（阿德莱德大学）；University of Potsdam（波茨坦大学）
A popular paradigm for 3D point cloud registration is by extracting 3D keypoint correspondences, then estimating the registration function from the correspondences using a robust algorithm. However, many existing 3D keypoint techniques tend to produce large proportions of erroneous correspondences or outliers, which significantly increases the cost of robust estimation. An alternative approach is to directly search for the subset of correspondences that are pairwise consistent, without optimising the registration function. This gives rise to the combinatorial problem of matching with pairwise …
In this paper, we propose a coarse-to-fine integration solution inspired by the classical ICP algorithm, to pairwise 3D point cloud registration with two improvements of hybrid metric spaces (eg, BSC feature and Euclidean geometry spaces) and globally optimal correspondences matching. First, we detect the keypoints of point clouds and use the Binary Shape Context (BSC) descriptor to encode their local features. Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry. Ne…
Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Zhuke Innovation Technology（深圳珠科创新技术）
Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief prop…
Technical University of Munich（慕尼黑工业大学）；National University of Defense Technology（国防科技大学）；Siemens AG（西门子股份公司）
We study the problem of extracting correspondences between a pair of point clouds for registration. For correspondence retrieval, existing works benefit from matching sparse keypoints detected from dense points but usually struggle to guarantee their repeatability. To address this issue, we present CoFiNet - Coarse-to-Fine Network which extracts hierarchical correspondences from coarse to fine without keypoint detection. On a coarse scale and guided by a weighting scheme, our model firstly learns to match down-sampled nodes whose vicinity points share more overlap, which significantly shrinks …
Technische Universität München（慕尼黑工业大学）；Siemens AG（西门子股份公司）；National University of Defense Technology（国防科技大学）
We present a novel, data driven approach for solving the problem of registration of two point cloud scans. Our approach is direct in the sense that a single pair of corresponding local patches already provides the necessary transformation cue for the global registration. To achieve that, we first endow the state of the art PPF-FoldNet auto-encoder (AE) with a pose-variant sibling, where the discrepancy between the two leads to pose-specific descriptors. Based upon this, we introduce RelativeNet, a relative pose estimation network to assign correspondence-specific orientations to the keypoints,…
To eliminate the problems of large dimensional differences, big semantic gap, and mutual interference caused by hybrid features, in this paper, we propose a novel Multi-Features Guidance Network for partial-to-partial point cloud registration(MFG). The proposed network mainly includes four parts: keypoints' feature extraction, correspondences searching, correspondences credibility computation, and SVD, among which correspondences searching and correspondence credibility computation are the cores of the network. Unlike the previous work, we utilize the shape features and the spatial coordinates…
School of Mechanical and Power Engineering, East China University of Science and Technology（华东理工大学机械与动力工程学院）
Correspondence-based point cloud registration is a cornerstone in geometric computer vision, robotics perception, photogrammetry and remote sensing, which seeks to estimate the best rigid transformation between two point clouds from the correspondences established over 3D keypoints. However, due to limited robustness and accuracy, current 3D keypoint matching techniques are very prone to yield outliers, probably even in very large numbers, making robust estimation for point cloud registration of great importance. Unfortunately, existing robust methods may suffer from high computational cost or…
For relocalization in large-scale point clouds, we propose the first approach that unifies global place recognition and local 6DoF pose refinement. To this end, we design a Siamese network that jointly learns 3D local feature detection and description directly from raw 3D points. It integrates FlexConv and Squeeze-and-Excitation (SE) to assure that the learned local descriptor captures multi-level geometric information and channel-wise relations. For detecting 3D keypoints we predict the discriminativeness of the local descriptors in an unsupervised manner. We generate the global descriptor by…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
In this report we present an unsupervised image registration framework, using a pre-trained deep neural network as a feature extractor. We refer this to zero-shot learning, due to nonoverlap between training and testing dataset (none of the network modules in the processing pipeline were trained specifically for the task of medical image registration). Highlights of our technique are: (a) No requirement of a training dataset (b) Keypoints i.e.locations of important features are automatically estimated (c) The number of key points in this model is fixed and can possibly be tuned as a hyperparam…
Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence
Institute of Robotics, Athena Research Center（机器人研究所，雅典研究中心）；School of ECE, NTUA（电子工程学院，NTUA）；HERON – Hellenic Robotics Center of Excellence（希腊机器人 excellence 中心）；MPI for Intelligent Systems（智能系统研究所）；ETH Zurich（苏黎世联邦理工学院）；Google（谷歌）
Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill pairwise registration and loop closure. Feature description is pe…
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；National Laboratory of Pattern Recognition（模式识别国家重点实验室）；Institute of Automation, CAS（中国科学院自动化研究所）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）；Academy of Mathematics and Systems Science, CAS（中国科学院数学与系统科学研究院）；UCAS（中国科学院大学）
This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems. In contrast to existing physics-based methods that merely consider spatial point information and ignore surface geometry, we explore geometry aware rigid-body dynamics to regulate the particle (point) motion, which results in more precise and robust registration. Our proposed method consists of four major modules. First, we leverage the graph signal processing (GSP) framework to define a new signature, (i.e., point response inten…
ImFusion GmbH（ImFusion有限责任公司）；Technical University of Munich（慕尼黑工业大学）
Multi-modal registration is a required step for many image-guided procedures, especially ultrasound-guided interventions that require anatomical context. While a number of such registration algorithms are already available, they all require a good initialization to succeed due to the challenging appearance of ultrasound images and the arbitrary coordinate system they are acquired in. In this paper, we present a novel approach to solve the problem of registration of an ultrasound sweep to a pre-operative image. We learn dense keypoint descriptors from which we then estimate the registration. We…
We present ObjectMatch, a semantic and object-centric camera pose estimator for RGB-D SLAM pipelines. Modern camera pose estimators rely on direct correspondences of overlapping regions between frames; however, they cannot align camera frames with little or no overlap. In this work, we propose to leverage indirect correspondences obtained via semantic object identification. For instance, when an object is seen from the front in one frame and from the back in another frame, we can provide additional pose constraints through canonical object correspondences. We first propose a neural network to …
East China University of Science and Technology（华东理工大学）；Shanghai FOCS Instrument Analysis Co., Ltd.（上海福斯仪器分析有限公司）
Correspondence-based point cloud registration is a cornerstone in robotics perception and computer vision, which seeks to estimate the best rigid transformation aligning two point clouds from the putative correspondences. However, due to the limited robustness of 3D keypoint matching approaches, outliers, probably in large numbers, are prone to exist among the correspondences, which makes robust registration methods imperative. Unfortunately, existing robust methods have their own limitations (e.g. high computational cost or limited robustness) when facing high or extreme outlier ratios, proba…
