Uncertainty Estimation for Pretrained Medical Image Registration Models via Transformation Equivariance
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；The Hawkes Institute, University College London（霍克斯研究所，伦敦大学学院）；AI Laboratory, Massachusetts Institute of Technology（人工智能实验室，麻省理工学院）
Accurate image registration is essential in many medical imaging applications, yet most deep registration networks provide little indication of when or where their predictions are unreliable. Existing uncertainty estimation approaches, such as Bayesian methods, ensembles, or MC-dropout, typically require architectural modifications or retraining, precluding their applicability to pretrained registration models. We propose an inference-time, model-agnostic uncertainty estimation framework that applies directly to any pretrained registration network. Our approach is grounded in the transformation equivariance property of image registration, which states that the underlying anatomical mapping should remain consistent under spatial perturbations of the input. Experiments across three pretrained registration models and four anatomical structures show that the resulting uncertainty maps consistently correlate with registration error and highlight unreliably aligned regions. This framework turns pretrained registration networks into risk-aware tools at test time, moving medical image registration closer to safe clinical and large-scale research deployment.
LLaMA-Reg: Using LLaMA 2 for Unsupervised Medical Image Registration
Faculty of Information Science Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息科学工程与自动化学院）；Taizhou Hospital of Zhejiang Province Affiliated with Wenzhou Medical University（温州医科大学附属浙江省台州医院）；Enze Hospital, Taizhou Enze Medical Centre (Group)（台州恩泽医疗中心（集团）恩泽医院）；The Second Affiliated Hospital of Dalian Medical University（大连医科大学附属第二医院）
CARL: A Framework for Equivariant Image Registration
UNC Chapel Hill（北卡罗来纳大学教堂山分校）；Université Gustave Eiffel（Gustave Eiffel 大学）；INRIA Paris（法国国家信息与自动化研究所巴黎分部）；University of Salzburg（萨尔茨堡大学）；Harvard Medical School（哈佛医学院）；UC San Diego（加州大学圣迭戈分校）
IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration
Univ. Rennes, CLCC Eugene Marquis, INSERM, LTSI - UMR 1099（里昂大学、CLCC Eugene Marquis、INSERM、LTSI - UMR 1099）；CSIRO Australian e-Health Research Centre（澳大利亚e-Health研究中心）；CHU Rennes, Department of Cardio-Thoracic and Vascular Surgery（里昂大学医院、心胸和血管外科部门）
LISMARS；Hubei Luojia Laboratory（湖北珞珈实验室）；Wuhan University（武汉大学）；The University of Hong Kong（香港大学）；The Hong Kong Polytechnic University（香港理工大学）；Texas A&M University（德克萨斯农工大学）
We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations). CoMIRs enable the registration of multimodal images where existing registration methods often fail due to a lack of sufficiently similar image structures. CoMIRs reduce the multimodal registration problem to a monomodal one, in which general intensity-based, as well as feature-based, registration algorithms can be applied. The method involves training one neural network per modality on aligned images, using a contrastive loss based on noise-contrasti…
Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
Faculty of Electrical Engineering, KU Leuven（KU Leuven 电气工程学院）；Spatial Intelligence Group, The Hong Kong Polytechnic University（香港理工大学 空间智能组）；Faculty of Engineering and IT, The University of Melbourne（墨尔本大学 工程与信息技术学院）；IoTUS Lab, Queen Mary University of London（伦敦大学玛丽女王学院 IoTUS实验室）
Institute of Mathematics（数学研究所）；Image Computing, University of Luebeck, Luebeck, Germany（图像计算，吕贝克大学，吕贝克，德国）；Department of Applied Mathematics（应用数学系）；Theoretical Physics, University of Cambridge, Cambridge, United Kingdom（理论物理，剑桥大学，剑桥，英国）
MedDIFT: Multi-Scale Diffusion-Based Correspondence in 3D Medical Imaging
1 School of Computation, Information；Technology, Technical University of Munich 2 Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich 3 Munich Center for Machine Learning (MCML) 4 Institute for Diagnostic；Interventional Radiology, Klinkum Rechts der Isar 5 School of Biomedical Engineering \& Imaging Sciences, King’s College London
Locating semantically meaningful landmark points is a crucial component of a large number of computer vision pipelines. Because of the small number of available datasets with ground truth landmark annotations, it is important to design robust unsupervised and semi-supervised methods for landmark detection. Many of the recent unsupervised learning methods rely on the equivariance properties of landmarks to synthetic image deformations. Our work focuses on such widely used methods and sheds light on its core problem, its inability to produce equivariant intermediate convolutional features. This …
HAPS: Rethinking Image Similarity for Virtual Staining
Skolkovo Institute of Science and Technology（斯克洛洛沃科学与技术研究所）；University of Sharjah（沙迦大学）；National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation（俄罗斯联邦卫生部国家医学研究放射中心）
Medical University of Vienna（维也纳医科大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）
Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression. Self-supervised learning allows to learn new representation from available large unlabelled data without any expert knowledge. We propose a deep learning self-supervised approach to model disease progression from longitudinal retinal optical coherence tomography (OCT). Our self-supervised model takes benefit from a generic time-related task, by learning to estimate the time interval between pairs of scans acquired from the sa…
SINTEF（挪威科学与工业研究基金会）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；Oslo University Hospital（奥斯陆大学医院）
Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging. Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting. Results: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained m…
Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of Washington（华盛顿大学）；Colorado State University（科罗拉多州立大学）；National Geospatial-Intelligence Agency（国家地理空间情报局）
It is often said that a deep learning model is "invariant" to some specific type of transformation. However, what is meant by this statement strongly depends on the context in which it is made. In this paper we explore the nature of invariance and equivariance of deep learning models with the goal of better understanding the ways in which they actually capture these concepts on a formal level. We introduce a family of invariance and equivariance metrics that allows us to quantify these properties in a way that disentangles them from other metrics such as loss or accuracy. We use our metrics to…
A Two-Step Deep Learning Method for 3DCT-2DUS Kidney Registration During Breathing
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（信息通信研究院（I2R），新加坡科学、技术与研究局（A*STAR））；The Fifth Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第五医院）；Creative Medtech Solutions Pte Ltd（创意医疗科技解决方案私人有限公司）
Rapid patient-specific neural networks for intraoperative X-ray to volume registration
Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology（哈佛-麻省理工健康科学与技术, 麻省理工学院）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（计算机科学与人工智能实验室, 麻省理工学院）；Department of Radiology, Harvard Medical School（哈佛医学院放射科）；Saint Luke’s Marion Bloch Neuroscience Institute（圣路易斯马里恩布洛克神经科学研究所）；Department of Critical Care Medicine, Shriners Children’s Hospital（谢尔曼儿童医院重症医学科）；Department of Interventional Neuroradiology, Boston Children’s Hospital（波士顿儿童医院介入神经放射科）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（阿提努拉A·马丁诺斯生物医学成像中心, 麻省总医院）
Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring
Artificial Intelligence Lab, Otto-von-Guericke University Magdeburg（马格德堡大学人工智能实验室）；University of Rostock（罗斯托克大学）；Becker Lab, University of Marburg（马尔堡大学贝克尔实验室）
Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation
Interdisciplinary Program in Bioengineering, Seoul National University（首尔大学生物工程跨学科项目）；Department of Transdisciplinary Medicine, Seoul National University Hospital（首尔大学医院跨学科医学系）；Department of Artificial Intelligence, Yonsei University（延世大学人工智能系）；Department of Radiology, Seoul National University Hospital（首尔大学医院放射科）
