Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Universität zu Lübeck（吕贝克大学）
Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sul… 展开作者
Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Pew-Thian Yap, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Jens Sjölund, Daniel Grzech, Huaqi Qiu, Zeju Li, Alexander Thorley, Jinming Duan, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett Landman, Yuankai Huo, Keelin Murphy, Nikolas Lessmann, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.
Inception Institute of Artificial Intelligence（人工智能 inception 研究所）
Medical image registration is an important task in automated analysis of multi-modal images and temporal data involving multiple patient visits. Conventional approaches, although useful for different image types, are time consuming. Of late, deep learning (DL) based image registration methods have been proposed that outperform traditional methods in terms of accuracy and time. However,DL based methods are heavily dependent on training data and do not generalize well when presented with images of different scanners or anatomies. We present a DL based approach that can perform medical image regi…
Indian Institute of Technology Bombay（印度理工学院孟买分校）；Whirlpool（惠而浦）
We present WSSAMNet, a weakly supervised method for medical image registration. Ours is a two step method, with the first step being the computation of segmentation masks of the fixed and moving volumes. These masks are then used to attend to the input volume, which are then provided as inputs to a registration network in the second step. The registration network computes the deformation field to perform the alignment between the fixed and the moving volumes. We study the effectiveness of our technique on the BraTSReg challenge data against ANTs and VoxelMorph, where we demonstrate that our me…
University College London（伦敦大学学院）；InstaDeep；King’s College London（伦敦国王学院）
DeepReg (https://github.com/DeepRegNet/DeepReg) is a community-supported open-source toolkit for research and education in medical image registration using deep learning.
Johns Hopkins Medical Institutes（约翰斯·霍普金斯医学研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；NVIDIA Corporation（英伟达公司）
Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration. Recent advancements in designing registration Transformers have focused on using cross-attention (CA) to enable a more precise understanding of spatial correspondences between moving and fixed images. Here, we propose a novel CA mechanism that computes windowed attention using deformable windows. In contrast to existing CA mechanisms that require intensive computational complexity by either computing CA globally or locally with a f…
Institute of Mechatronic Systems, Leibniz Universität Hannover（汉诺威莱布尼茨大学机电系统研究所）
We present deformable unsupervised medical image registration using a randomly-initialized deep convolutional neural network (CNN) as regularization prior. Conventional registration methods predict a transformation by minimizing dissimilarities between an image pair. The minimization is usually regularized with manually engineered priors, which limits the potential of the registration. By learning transformation priors from a large dataset, CNNs have achieved great success in deformable registration. However, learned methods are restricted to domain-specific data and the required amounts of me…
Institute of Medical Informatics, Universität zu Lübeck（吕贝克大学医学信息学研究所）
Though, deep learning based medical image registration is currently starting to show promising advances, often, it still fells behind conventional frameworks in terms of registration accuracy. This is especially true for applications where large deformations exist, such as registration of interpatient abdominal MRI or inhale-to-exhale CT lung registration. Most current works use U-Net-like architectures to predict dense displacement fields from the input images in different supervised and unsupervised settings. We believe that the U-Net architecture itself to some level limits the ability to p…
multiGradICON: A Foundation Model for Multimodal Medical Image Registration
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Salzburg（萨尔茨堡大学）；Université Gustave Eiffel（古斯塔夫·埃菲尔大学）；Brigham and Women’s Hospital（布莱根妇女医院）；ÉTS Montréal（蒙特利尔工程学院）；Boston University（波士顿大学）；Kitware Inc.（Kitware公司）
This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration. In contrast to existing approaches that learn spatial transformations from training data in the high dimensional imaging space, we develop a new registration network entirely in a low dimensional bandlimited space. This dramatically reduces the computational cost and memory footprint of an expensive training and inference. To achieve this goal, we first introduce complex-valued operations and representations of neural architectures that provide key components for …
Shiraz University of Medical Sciences（设拉子医科大学）；University of Pittsburgh School of Medicine（匹兹堡大学医学院）；UPMC Hillman Cancer Center（UPMC希尔曼癌症中心）；Shiraz University of Technology（设拉子理工大学）；Mashhad University of Medical Science（马什哈德医科大学）
In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration in this area. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy, playing a key role in countering tumors during the operation. The current study presents a comprehe…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医疗中心）；University of Cincinnati College of Medicine（辛辛那提大学医学院）；University of Cincinnati（辛辛那提大学）
Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration. Firstly, a discussion is provided for supervised registration categories, for example, fully supervised, dual supervised, and weakly supervised registration. Next, similarity-based as well as generative adversarial network (GAN)-based registration are presented as part of unsupervised registrat…
University of Sao Paulo（圣保罗大学）；Faculty of Philosophy, Science and Languages of Ribeirao Preto（里贝朗普雷图哲学、科学与语言学院）；Department of Physics Applied to Medicine and Biology（医学与生物学应用物理系）
Mutual information (MI) is the standard method used in image registration and the most studied one but can diverge and produce wrong results when used in an automated manner. In this study we compared the results of the ITK Mattes MI function, used in 3D Slicer and ITK derived software solutions, and our own MICUDA Shannon and Tsallis MI functions under the translation, rotation and scale transforms in a 3D mathematical space. This comparison allows to understand why registration fails in some circumstances and how to produce a more robust automated algorithm to register medical images. Since …
Johns Hopkins University（约翰斯·霍普金斯大学）；Johns Hopkins Medical Institutes（约翰斯·霍普金斯医学研究所）
In the last decade, convolutional neural networks (ConvNets) have dominated and achieved state-of-the-art performances in a variety of medical imaging applications. However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image. The recently proposed Vision Transformer (ViT) for image classification uses a purely self-attention-based model that learns long-range spatial relations to focus on the relevant parts of an image. Nevertheless, ViT emphasizes the low-resolution features because of the consecutive downsamplings, result i…
Institute of Parallel and Distributed Systems, University of Stuttgart（斯图加特大学并行与分布式系统研究所）
Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost the registration performance in both accuracy and runtime, we propose a fast convolutional neural network. Specially, to efficiently utilize the memory resources and enlarge the model capacity, we adopt additive forwarding instead of channel concatenation and deepen the network in each enc…
We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ult…
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…
A Symmetric Dynamic Learning Framework for Diffeomorphic Medical Image Registration
Xiangtan University（湘潭大学）；University of Strathclyde（思克莱德大学）；Nankai University（南开大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Manchester（曼彻斯特大学）
Institute for Parallel and Distributed Systems, University of Stuttgart（斯图加特大学并行与分布式系统研究所）
Image registration is a fundamental task for medical imaging. Resampling of the intensity values is required during registration and better spatial resolution with finer and sharper structures can improve the resampling performance and hence the registration accuracy. Super-resolution (SR) is an algorithmic technique targeting at spatial resolution enhancement which can achieve an image resolution beyond the hardware limitation. In this work, we consider SR as a preprocessing technique and present a CNN-based resolution enhancement module (REM) which can be easily plugged into the registration…
University of Birmingham（伯明翰大学）；University of Melbourne（墨尔本大学）；University of Warwick（华威大学）；Northeast Forestry University（东北林业大学）；Alan Turing Institute（艾伦·图灵研究所）
Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to capture accurate deformations without needing long-range dependencies. The purpose of this study is therefore to investigate whether U-Net-based methods are outdated compared to modern transformer-based approaches when applied to medical image registration. For this, we propose a large kernel U-Net (LKU-Net) by embedding a parallel convolutional bl…
Queensland University of Technology（昆士兰科技大学）；CSIRO（联邦科学与工业研究组织）
The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts. Most of the learning-based registration approaches considers this task as a one directional problem. As a result, only correspondence from the moving image to the target image is considered. However, in some medical procedures bidirectional registration is required to be performed. Unlike other learning-based registration, we propose a registration framework with invers…
Johns Hopkins Medical Institutes（约翰·霍普金斯医学院）；Johns Hopkins University（约翰·霍普金斯大学）；NVIDIA Corporation（英伟达公司）；Duke University Medical Center（杜克大学医学中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehensi…
Huazhong University of Science and Technology（华中科技大学）；College of Life Science and Technology, Huazhong University of Science and Technology（华中科技大学生命科学与技术学院）
Deformable medical image registration plays an important role in clinical diagnosis and treatment. Recently, the deep learning (DL) based image registration methods have been widely investigated and showed excellent performance in computational speed. However, these methods cannot provide enough registration accuracy because of insufficient ability in representing both the global and local features of the moving and fixed images. To address this issue, this paper has proposed the transformer based image registration method. This method uses the distinctive transformer to extract the global and…
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…
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…
Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Imperial College London（伦敦帝国理工学院）；University of Pennsylvania（宾夕法尼亚大学）；National University of Singapore（新加坡国立大学）
Southeast University（东南大学）；Tencent Healthcare Co.（腾讯医疗健康有限公司）；The Chinese University of Hong Kong（香港中文大学）
Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to spatial transformation through the convolutional neural network, ignoring its limited ability to capture spatial correspondence. On the other hand, Transformer can better characterize the spatial relationship with attention mechanism, its long-range dependency may be harmful to the registration task, where voxels with too large distances are unlikely to be corresponding pairs. In this study, we propose a novel Deforme…
In this research paper, authors propose multimodal brain image registration using discrete wavelet transform(DWT) followed by Gaussian pyramids. The reference and target images are decomposed into their LL, LH, HL and LL DWT coefficients and then are processed for image registration using Gaussian pyramids. The image registration is also done using Gaussian pyramids only and wavelets transforms only for comparison. The quality of registration is measured by comparing the maximum MI values used by the three methods and also by comparing their correlation coefficients. Our proposed technique pro…
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.
Emory University（埃默里大学）；Winship Cancer Institute, Emory University（埃默里大学温希普癌症研究所）
This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive compar…
From Model Based to Learned Regularization in Medical Image Registration: A Comprehensive Review
School of Computation, Information and Technology, Technical University of Munich (TUM)（慕尼黑工业大学计算、信息与技术学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）；School of Medicine, Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院医学院）
Image registration aims to establish spatial correspondence across pairs, or groups of images, and is a cornerstone of medical image computing and computer-assisted-interventions. Currently, most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous, which is not always valid for real-world scenarios, especially in medical image registration (e.g. cardiac imaging and abdominal imaging). Such a global constraint can lead to artefacts and increased errors at discontinuous tissue interfaces. To tackle this issue, we propose a weakly…
the University of Sydney（悉尼大学）；Royal Prince Alfred Hospital（皇家阿尔弗雷德王子医院）；Shanghai Jiao Tong University（上海交通大学）
Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally opt…
Markerless Tracking-Based Registration for Medical Image Motion Correction
Department Artificial Intelligence in Biomedical Engineering, FAU Erlangen-Nürnberg（生物医学工程人工智能系，埃尔朗根-纽伦堡大学）；Department of Communication Sciences and Disorders, East Carolina University（通讯科学与障碍系，东卡罗来纳大学）；Department of Otolaryngology–Head and Neck Surgery, Louisiana State University Health Sciences Center（耳鼻喉科与头颈外科系，路易斯安那州立大学健康科学中心）；Department of Radiology, FAU Erlangen-Nürnberg（放射学系，埃尔朗根-纽伦堡大学）；Department of Communication Sciences and Disorders, Louisiana State University（通讯科学与障碍系，路易斯安那州立大学）
Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom, therefore the generated dataset can serv…
University College London（伦敦大学学院）；University of Leeds（利兹大学）
Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world clinical applications, such as the MR-to-ultrasound registration for prostate intervention of interest in this work, but also provides an explainable means of understanding the organ motion and spatial correspondence establishment. This work instantiates the recently-proposed physics-informed neural networks (PINNs) to a 3D linear elastic model for …
Image registration is a key operation in medical image processing, allowing a plethora of applications. Mutual information (MI) is consolidated as a robust similarity metric often used for medical image registration. Although MI provides a robust medical image registration, it usually fails when the needed image transform is too big due to MI local maxima traps. In this paper, we propose and evaluate a generalized parametric MI as an affine registration cost function. We assessed the generalized MI (GMI) functions for separable affine transforms and exhaustively evaluated the GMI mathematical …
Search-MIND: Training-Free Multi-Modal Medical Image Registration
Lab for Uncertainty in Data and Decision Making (LUCID), Nottingham Biomedical Research Centre (BRC), School of Medicine, University of Nottingham, UK（英国诺丁汉大学医学院诺丁汉生物医学研究中心（BRC）数据与决策不确定性实验室（LUCID））
Deformable Medical Image Registration with Effective Anatomical Structure Representation and Divide-and-Conquer Network
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（集成空天地海大数据应用技术国家工程实验室，计算机科学与工程学院，西北工业大学）
Deep Learning in Medical Image Registration: Magic or Mirage?
Computer and Information Science（计算机与信息科学学院）；Electrical and Systems Engineering（电气与系统工程学院）；Radiology（放射学系）；Penn Image Computing and Science Laboratory（宾夕法尼亚图像计算与科学实验室）
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）
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 …
