We introduce a fluid-based image augmentation method for medical image analysis. In contrast to existing methods, our framework generates anatomically meaningful images via interpolation from the geodesic subspace underlying given samples. Our approach consists of three steps: 1) given a source image and a set of target images, we construct a geodesic subspace using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model; 2) we sample transformations from the resulting geodesic subspace; 3) we obtain deformed images and segmentations via interpolation. Experiments on brain (LPBA) and knee (OAI) data illustrate the performance of our approach on two tasks: 1) data augmentation during training and testing for image segmentation; 2) one-shot learning for single atlas image segmentation. We demonstrate that our approach generates anatomically meaningful data and improves performance on these tasks over competing approaches. Code is available at https://github.com/uncbiag/easyreg.
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Radboud University Medical Center（拉德堡德大学医学中心）
Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-py…
SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid
DAMO Academy, Alibaba Group（达摩院，阿里巴巴集团）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；The First Affiliated Hospital of College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；Hupan Lab（华潘实验室）
Polaffini: A feature-based approach for robust affine and polyaffine image registration
Hawkes Institute & Department of Computer Science, University College London, London, UK（霍克斯研究所及大学学院伦敦计算机科学系，伦敦，英国）；Institut Pasteur, Université Paris Cité, Unité de Neuroanatomie Appliquée et Théorique（巴斯德研究所，巴黎城市大学，应用与理论神经解剖学单元）；AINOSTICS ltd., Manchester, UK（AINOSTICS有限公司，曼彻斯特，英国）
Johns Hopkins University（约翰斯·霍普金斯大学）；PAII Inc.（PAII公司）；Ping An Technology（平安科技）；The First Affiliated Hospital Zhejiang University（浙江大学医学院附属第一医院）
In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm SAM, which is capable of computing dense anatomical/semantic correspondences between two images at the pixel level. Our method is named SAME, which breaks down image registration into three steps: affine transformation, coarse deformation, and deep deformable registration. Using SAM embeddings, we enhance these steps by finding more coherent correspondences, and providing features and a loss function with better semantic guidance. We collect a mu…
Research institute for signals, systems and computational intelligence, sinc(i), FICH-UNL/CONICET（信号、系统与计算智能研究所 sinc(i)，FICH-UNL/CONICET）；University of Oxford（牛津大学）；TheraPanacea；CentraleSupelec（中央理工-高等电力学院）；Universite Paris-Saclay（巴黎-萨克雷大学）
Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results. In this paper, we propose a…
Beijing Institute of Technology（北京理工大学）；Cranfield University（克兰菲尔德大学）；Science and Technology on Space Physics Laboratory（空间物理重点实验室）
Augmented reality technology based on image registration is becoming increasingly popular for the convenience of pre-surgery preparation and medical education. This paper focuses on the registration of forearm images and digital anatomical models. Due to the difference in texture features of forearm multi-modal images, this paper proposes a forearm feature representation curve (FFRC) based on structure compliant multi-modal image registration framework (FAM) for the forearm.
Continuous sPatial-Temporal Deformable Image Registration (CPT-DIR) for motion modelling in radiotherapy: beyond classic voxel-based methods
Center for Proton Therapy, Paul Scherrer Institut（保罗·施特格研究所质子治疗中心）；Department of Computer Science, ETH Zürich（苏黎世联邦理工学院计算机科学系）；Department of Physics, ETH Zürich（苏黎世联邦理工学院物理系）；Department of Automation, Tsinghua Univeristy（清华大学自动化系）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）
Spatially Covariant Image Registration with Text Prompts
College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Cornell University（康奈尔大学）；University of Pennsylvania（宾夕法尼亚大学）；New York University（纽约大学）；Shanghai Jiao Tong University（上海交通大学）
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Korea University（高丽大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）
Objective: Deformable brain MR image registration is challenging due to large inter-subject anatomical variation. For example, the highly complex cortical folding pattern makes it hard to accurately align corresponding cortical structures of individual images. In this paper, we propose a novel deep learning way to simplify the difficult registration problem of brain MR images. Methods: We train a morphological simplification network (MS-Net), which can generate a "simple" image with less anatomical details based on the "complex" input. With MS-Net, the complexity of the fixed image or the movi…
Unsupervised learning of spatially varying regularization for diffeomorphic image registration
Department of Radiology and Radiological Science, Johns Hopkins School of Medicine, MD, USA（放射科与放射科学系，约翰霍普金斯医学院，马里兰州，美国）；Image Analysis and Communications Laboratory, Department of Electrical and Computer Engineering, Johns Hopkins University, MD, USA（图像分析与通信实验室，电气与计算机工程系，约翰霍普金斯大学，马里兰州，美国）；Department of Electrical and Computer Engineering, Vanderbilt University, TN, USA（电气与计算机工程系，范德比尔特大学，田纳西州，美国）；NVIDIA Corporation, Santa Clara, CA, USA（英伟达公司，圣克拉拉，加利福尼亚州，美国）
A Multimodal Clinically Informed Coarse-to-Fine Framework for Longitudinal CT Registration in Proton Therapy
Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA（梅奥诊所放射肿瘤科）；School of Biomedical Engineering, ShanghaiTech University, Shanghai, China（上海科技大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China（上海联合影像智能科技有限公司）；Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China（上海人工智能实验室）；Shanghai Clinical Research and Trial Center, Shanghai 201210, China（上海临床研究与试验中心）
MORPH-LER: Log-Euclidean Regularization for Population-Aware Image Registration
Karlert School of Computing, University of Utah（犹他大学卡尔尔特计算学院）；Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；Biomedical Engineering Department, University of Utah（犹他大学生物医学工程系）
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；School of Computing, University of Utah（犹他大学计算学院）
Spatial transformations are enablers in a variety of medical image analysis applications that entail aligning images to a common coordinate systems. Population analysis of such transformations is expected to capture the underlying image and shape variations, and hence these transformations are required to produce anatomically feasible correspondences. This is usually enforced through some smoothness-based generic regularization on deformation field. Alternatively, population-based regularization has been shown to produce anatomically accurate correspondences in cases where anatomically unaware…
University College London（伦敦大学学院）；University of Oxford（牛津大学）
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks. The proposed end-t…
University College London（伦敦大学学院）；University of Oxford（牛津大学）
We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distin…
FMIR, a foundation model-based Image Registration Framework for Robust Image Registration
School of Artificial Intelligence and Robotics（人工智能与机器人学院）；Hunan University（湖南大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Cornell University（康奈尔大学）
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…
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…
ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究中心）
Image registration of liver dynamic contrast-enhanced computed tomography (DCE-CT) is crucial for diagnosis and image-guided surgical planning of liver cancer. However, intensity variations due to the flow of contrast agents combined with complex spatial motion induced by respiration brings great challenge to existing intensity-based registration methods. To address these problems, we propose a novel structure-aware registration method by incorporating structural information of related organs with segmentation-guided deep registration network. Existing segmentation-guided registration methods …
Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpropagation during training. However, intensity-based metrics can be misleading when the assumption of intensity class correspondence is violated, especially in cross-modality or contrast-enhanced images. Moreover, existing learning-based registration methods are predominantly applicable to pairwise registration and are rarely extended to groupwise registration or simultaneous re…
Johns Hopkins Medical Institutes（约翰霍普金斯医学研究所）；Johns Hopkins University（约翰霍普金斯大学）
Objectives: Computerized phantoms play an essential role in various applications of medical imaging research. Although the existing computerized phantoms can model anatomical variations through organ and phantom scaling, this does not provide a way to fully reproduce anatomical variations seen in humans. However, having a population of phantoms that models the variations in patient anatomy and, in nuclear medicine, uptake realization is essential for comprehensive validation and training. In this work, we present a novel image registration method for creating highly anatomically detailed anthr…
The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge
Inria Saclay Île-de-France（法国里昂萨克利研究所）；CEA（法国原子能委员会）；Université Paris-Saclay（巴黎-萨克利大学）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑研究所）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化技术研究所）；Inserm（法国国家医学研究院）；AP-HP（法国国家医院集团）；Hôpital de la Pitié Salpêtrière（皮蒂埃-萨尔普里埃尔医院）；Harvard Medical School（哈佛医学院）；Brigham and Women's Hospital（布里根医院）；Department of Radiology and Radiological Science（放射学与放射科学系）；Johns Hopkins Medical School（约翰霍普金斯医学院）；Institute of Medical Informatics（医学信息学研究所）；University of Lübeck（吕贝克大学）；Image Analysis and Communications Laboratory（图像分析与通信实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Johns Hopkins University（约翰霍普金斯大学）；CSAIL（媒体实验室）
Westfälische Hochschule University of Applied Sciences（威斯特伐利亚应用科学大学）
Central venous catheters (CVC) are commonly inserted into the large veins of the neck, e.g. the internal jugular vein (IJV). CVC insertion may cause serious complications like misplacement into an artery or perforation of cervical vessels. Placing a CVC under sonographic guidance is an appropriate method to reduce such adverse events, if anatomical landmarks like venous and arterial vessels can be detected reliably. This task shall be solved by registration of patient individual images vs. an anatomically labelled reference image. In this work, a linear, affine transformation is performed on c…
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
Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024
College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）
University College London（伦敦大学学院）；University of Oxford（牛津大学）
Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in …
University of Edinburgh（爱丁堡大学）；Imperial College London（帝国理工学院）；University of Essex（埃塞克斯大学）
Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, …
Joint cortical registration of geometry and function using semi-supervised learning
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；McGovern Institute for Brain Research（麦戈文脑科学研究所）；Harvard University（哈佛大学）
University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）
Deep convolutional neural networks (CNNs) are state-of-the-art for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. Motivated by classical approaches for joint segmentation and registration we therefore propose a deep learning framework that jointly learns networks for image registration and image segmentation. In contrast to previous work on deep unsupervised image registration, which showed the benefit of weak supervision via image segmentations, our approac…
SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Alibaba Group（阿里巴巴集团）；Johns Hopkins University（约翰斯·霍普金斯大学）；The First Affiliated Hospital of Zhejiang University（浙江大学附属第一医院）
TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research
University of Strasbourg（斯特拉斯堡大学）；Research Institute against Digestive Cancer (IRCAD)（消化癌症研究所（IRCAD））；Conservatoire National des Arts et Métiers (CNAM)（法国国立工艺学院（CNAM））；Sorbonne University（索邦大学）；Clinique Sainte-Anne（圣安妮诊所）；Clinique Saint Augustin（圣奥古斯丁诊所）；Institute of Image-Guided Surgery (IHU)（图像引导手术研究所（IHU））
Benchmarking Intra-Patient 3D Deformable Multimodal Image Registration
IMAG2, Institut IMAGINE, Université Paris Cité（IMAG2，IMAGINE研究所，巴黎西岱大学）；Replico SAS（Replico公司）；Université Paris Cité, Department of Pediatric Surgery, Hôpital Necker Enfants-Malades, Assistance Publique–Hôpitaux de Paris (AP-HP)（巴黎西岱大学，内克尔儿童医院小儿外科，巴黎公立医院集团）；Sorbonne Université, CNRS, LIP6（索邦大学，法国国家科学研究中心，LIP6）；LTCI, Télécom Paris, Institut Polytechnique de Paris（LTCI，巴黎电信，巴黎综合理工学院）
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 with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. The goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures. We argue and show that the current direct, non-iterative approaches are sub-optimal, in particular if we seek accurate alignment of Structures-of-Interest (SoI). Information about SoI is often available at training time, for example, in form of segmentations or lan…
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Department of Imaging, Radboudumc, Nijmegen, the Netherlands（影像部，Radboudumc，尼姆egen，荷兰）；Institute for Medical Informatics, University of Lübeck, Lübeck, Germany（医学信息研究所，吕贝克大学，吕贝克，德国）；Department of Nuclear Medicine, Hospital 108, Hanoi, Vietnam（核医学部，108医院，河内，越南）；Department of Diagnostic Imaging and Interventional Radiology, Thai Nguyen National Hospital, Thai Nguyen, Vietnam（诊断影像和介入放射科，Thai Nguyen国家医院，Thai Nguyen，越南）；Interventional Radiology Center, Tam Anh Hospital, Hanoi, Vietnam（介入放射科中心，Tam Anh医院，河内，越南）；FET, Vietnam National University, University of Engineering and Technology, Hanoi, Vietnam（FET，越南国家大学，工程与技术大学，河内，越南）
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…
FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）；University of Leeds（利兹大学）
Comparison of microvascular circulation on fundoscopic images is a non-invasive clinical indication for the diagnosis and monitoring of diseases, such as diabetes and hypertensions. The differences between intra-patient images can be assessed quantitatively by registering serial acquisitions. Due to the variability of the images (i.e. contrast, luminosity) and the anatomical changes of the retina, the registration of fundus images remains a challenging task. Recently, several deep learning approaches have been proposed to register fundus images in an end-to-end fashion, achieving remarkable re…
The Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutions（约翰斯·霍普金斯医疗机构）
Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video…
University of Utah（犹他大学）；University of Maryland, School of Medicine（马里兰大学医学院）
Radiation therapy presents a need for dynamic tracking of a target tumor volume. Fiducial markers such as implanted gold seeds have been used to gate radiation delivery but the markers are invasive and gating significantly increases treatment time. Pretreatment acquisition of a respiratory correlated 4DCT allows for determination of accurate motion tracking which is useful in treatment planning. We design a patient-specific motion subspace and a deep convolutional neural network to recover anatomical positions from a single fluoroscopic projection in real-time. We use this deep network to appr…
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）
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）
Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based registration methods have demonstrated superior performance compared to iterative methods in just a fraction of the time. Most of the learning-based methods have focused on mono-modal image registration. The extension to multi-modal registration depends on the use of an appropriate similarity function, such as the mutual information (MI). We propose guiding the training of a deep learning-based registration method with MI…
Radiological imaging of prostate is becoming more popular among researchers and clinicians in searching for diseases, primarily cancer. Scans might be acquired at different times, with patient movement between scans, or with different equipment, resulting in multiple datasets that need to be registered. For this issue, we introduce a registration method using anatomical feature-guided mutual information. Prostate scans of the same patient taken in three different orientations are first aligned for the accurate detection of anatomical features in 3D. Then, our pipeline allows for multiple modal…
In nuclear imaging, limited resolution causes partial volume effects (PVEs) that affect image sharpness and quantitative accuracy. Partial volume correction (PVC) methods incorporating high-resolution anatomical information from CT or MRI have been demonstrated to be effective. However, such anatomical-guided methods typically require tedious image registration and segmentation steps. Accurately segmented organ templates are also hard to obtain, particularly in cardiac SPECT imaging, due to the lack of hybrid SPECT/CT scanners with high-end CT and associated motion artifacts. Slight mis-regist…
VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH（VRVis虚拟现实与可视化研究中心有限公司）；Agfa NV（爱克发股份有限公司）；Antwerp University Hospital（安特卫普大学医院）；University of Antwerp（安特卫普大学）
To facilitate both the detection and the interpretation of findings in chest X-rays, comparison with a previous image of the same patient is very valuable to radiologists. Today, the most common approach for deep learning methods to automatically inspect chest X-rays disregards the patient history and classifies only single images as normal or abnormal. Nevertheless, several methods for assisting in the task of comparison through image registration have been proposed in the past. However, as we illustrate, they tend to miss specific types of pathological changes like cardiomegaly and effusion.…
Research institute for signals, systems and computational intelligence, sinc(i)（信号、系统与计算智能研究所（sinc(i)））；FICH-UNL（国立罗萨里奥大学医学与生物化学研究所）；CONICET（阿根廷国家科学与技术研究委员会）
Deformable image registration is a fundamental problem in the field of medical image analysis. During the last years, we have witnessed the advent of deep learning-based image registration methods which achieve state-of-the-art performance, and drastically reduce the required computational time. However, little work has been done regarding how can we encourage our models to produce not only accurate, but also anatomically plausible results, which is still an open question in the field. In this work, we argue that incorporating anatomical priors in the form of global constraints into the learni…
