Accurate cardiac ultrasound segmentation is essential for reliable assessment of ventricular function in intelligent healthcare systems. However, echocardiographic images are challenging due to low contrast, speckle noise, irregular boundaries, and domain shifts across devices and patient populations. Existing methods, largely based on appearance-driven learning, often fail to preserve boundary precision and structural consistency under these conditions. To address these issues, we propose a Contour-Guided Query Refinement Network (CGQR-Net) for boundary-aware cardiac ultrasound segmentation. The framework integrates multi-resolution feature representations with contour-derived structural priors. An HRNet backbone preserves high-resolution spatial details while capturing multi-scale context. A coarse segmentation is first generated, from which anatomical contours are extracted and encoded into learnable query embeddings. These contour-guided queries interact with fused feature maps via cross-attention, enabling structure-aware refinement that improves boundary delineation and reduces noise artifacts. A dual-head supervision strategy jointly optimizes segmentation and boundary prediction to enforce structural consistency. The proposed method is evaluated on the CAMUS dataset and further validated on the CardiacNet dataset to assess cross-dataset generalization. Experimental results demonstrate improved segmentation accuracy, enhanced boundary precision, and robust performance across varying imaging conditions. These results highlight the effectiveness of integrating contour-level structural information with feature-level representations for reliable cardiac ultrasound segmentation.
Technion - Israel Institute of Technology（以色列理工学院）；University of Waterloo（滑铁卢大学）；Hillel Yaffe Medical Center（希勒尔·雅菲医疗中心）
Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received lines are obtained from each wide-focused transmitted line. This shortens the acquisition time at the expense of introducing block artifacts. In this paper, we propose a data-driven learning-based approach to improve the MLA image quality. We train an end-to-end convolutional neural network on pairs of real ultrasound cardiac data, acquired through MLA and the corresponding single-line acquisition (SLA). The network …
Université Pierre et Marie Curie（皮埃尔和玛丽·居里大学）；SIGnal processing and MAchine learning Lab, ESPCI ParisTech, PSL Research University（巴黎高等物理化工学院信号处理与机器学习实验室，巴黎文理研究大学）；Vocal Tract Visualization Lab, University of Maryland Dental School（马里兰大学牙科学院声道可视化实验室）
Studying tongue motion during speech using ultrasound is a standard procedure, but automatic ultrasound image labelling remains a challenge, as standard tongue shape extraction methods typically require human intervention. This article presents a method based on deep neural networks to automatically extract tongue contour from ultrasound images on a speech dataset. We use a deep autoencoder trained to learn the relationship between an image and its related contour, so that the model is able to automatically reconstruct contours from the ultrasound image alone. In this paper, we use an automati…
Université Pierre et Marie Curie（皮埃尔和玛丽·居里大学）；Langevin Institute, ESPCI-ParisTech（巴黎高等物理化工学院朗之万研究所）；University of New Mexico（新墨西哥大学）；Tianjin University（天津大学）
This article describes a contour-based 3D tongue deformation visualization framework using B-mode ultrasound image sequences. A robust, automatic tracking algorithm characterizes tongue motion via a contour, which is then used to drive a generic 3D Finite Element Model (FEM). A novel contour-based 3D dynamic modeling method is presented. Modal reduction and modal warping techniques are applied to model the deformation of the tongue physically and efficiently. This work can be helpful in a variety of fields, such as speech production, silent speech recognition, articulation training, speech dis…
John Radcliffe Hospital（约翰·拉德克利夫医院）；Ultromics Ltd（Ultromics有限公司）；Friedrich--Alexander University Erlangen--Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Imperial College London（帝国理工学院）
Cardiac ultrasound imaging is used to diagnose various heart diseases. Common analysis pipelines involve manual processing of the video frames by expert clinicians. This suffers from intra- and inter-observer variability. We propose a novel approach to ultrasound video analysis using a transformer architecture based on a Residual Auto-Encoder Network and a BERT model adapted for token classification. This enables videos of any length to be processed. We apply our model to the task of End-Systolic (ES) and End-Diastolic (ED) frame detection and the automated computation of the left ventricular …
Semantic contour detection is a challenging problem that is often met in medical imaging, of which placental image analysis is a particular example. In this paper, we investigate utero-placental interface (UPI) detection in 2D placental ultrasound images by formulating it as a semantic contour detection problem. As opposed to natural images, placental ultrasound images contain specific anatomical structures thus have unique geometry. We argue it would be beneficial for UPI detectors to incorporate global context modelling in order to reduce unwanted false positive UPI predictions. Our approach…
Eindhoven University of Technology（埃因霍温理工大学）；Philips Research（飞利浦研究院）
Fast and accurate catheter detection in cardiac catheterization using harmless 3D ultrasound (US) can improve the efficiency and outcome of the intervention. However, the low image quality of US requires extra training for sonographers to localize the catheter. In this paper, we propose a catheter detection method based on a pre-trained VGG network, which exploits 3D information through re-organized cross-sections to segment the catheter by a shared fully convolutional network (FCN), which is called a Direction-Fused FCN (DF-FCN). Based on the segmented image of DF-FCN, the catheter can be loc…
The Research Center of Biomedical Engineering (CREB)（生物医学工程研究中心 (CREB)）；Universitat Politècnica de Cataluya（加泰罗尼亚理工大学）；École Normale Supérieure（巴黎高等师范学院）；Institute for Bioengineering of Catalonia（加泰罗尼亚生物工程研究所）；Radboud University Nijmegen Medical Centre（拉德堡德大学奈梅亨医学中心）；Eidgenössische Technische Hochschule Zürich - ETH Zurich（苏黎世联邦理工学院）
Cardiac motion estimation is an important diagnostic tool to detect heart diseases and it has been explored with modalities such as MRI and conventional ultrasound (US) sequences. US cardiac motion estimation still presents challenges because of the complex motion patterns and the presence of noise. In this work, we propose a novel approach to estimate the cardiac motion using ultrafast ultrasound data. -- Our solution is based on a variational formulation characterized by the L2-regularized class. The displacement is represented by a lattice of b-splines and we ensure robustness by applying a…
University of Oslo（奥斯陆大学）；GE Vingmed Ultrasound（GE维ingmed超声公司）；GE Research（通用电气研究中心）
Accurate and consistent predictions of echocardiography parameters are important for cardiovascular diagnosis and treatment. In particular, segmentations of the left ventricle can be used to derive ventricular volume, ejection fraction (EF) and other relevant measurements. In this paper we propose a new automated method called EchoGraphs for predicting ejection fraction and segmenting the left ventricle by detecting anatomical keypoints. Models for direct coordinate regression based on Graph Convolutional Networks (GCNs) are used to detect the keypoints. GCNs can learn to represent the cardiac…
EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation
Imperial College London（帝国理工学院）；University of Birmingham（伯明翰大学）；Ultromics Ltd.（奥特罗米克斯有限公司）；University of Oxford（牛津大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
The Chinese University of Hong Kong（香港中文大学）；The Hong Kong Polytechnic University（香港理工大学）；Shenzhen Institute of Advanced Technology, CAS（中国科学院深圳先进技术研究院）；National University of Singapore（新加坡国立大学）
Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Instrument Science and Engineering, Southeast University（东南大学仪器科学与工程学院）；Department of Biomedical Engineering, Case Western Reserve University（凯斯西储大学生物医学工程系）
Left ventricular segmentation is essential for measuring left ventricular function indices. Segmentation of one or several images requires an initial guess of the contour. It is hypothesized here that creating an initial guess by first detecting anatomical markers, would lead to correct detection of the endocardium. The first step of the algorithm presented here includes automatic detection of the mitral valve. Next, the apex is detected in the same frame. The valve is then tracked throughout the cardiac cycle. Contours passing from the apex to each valve corner are then found using a dynamic …
Romanian Institute of Science and Technology（罗马尼亚科学技术研究所）；University of Medicine and Pharmacy of Craiova（克拉约瓦医药大学）
The present paper puts forward an incipient study that uses a traditional segmentation method based on Zernike moments for extracting significant features from frames of fetal echocardiograms from first trimester color Doppler examinations. A distance based approach is then used on the obtained indicators to classify frames of three given categories that should be present in a normal heart condition. The computational tool shows promise in supporting the obstetrician in a rapid recognition of heart views during screening.
Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification
Danube Private University (DPU)（多瑙河私立大学）；Austrian Centre for Medical Innovation and Technology (ACMIT)（奥地利医学创新与技术中心）；Medical University of Vienna（维也纳医科大学）；Urmia University of Medical Science（乌尔米亚医科大学）
Unified Review and Benchmark of Deep Segmentation Architectures for Cardiac Ultrasound on CAMUS
Department of Computer Science；Artificial Intelligence, Dongguk University, Seoul 04620, Republic of Korea；Department of Semiconductor System Engineering, Sejong University, Seoul, 05006, Republic of Korea；Department of Operations Research；Statistics, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia；Department of Industrial Engineering \& Management, Yuan Ze University, Taoyuan City 320315, Taiwan；Department of Applied Mathematical Science, College of Science；Technology, Korea University, Sejong 30019, Republic of Korea
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；The Hamlyn Centre for Robotic Surgery, Imperial College of London（伦敦帝国理工学院哈姆林机器人手术中心）；Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）；Shanghai Xinhua Hospital（上海新华医院）
Echocardiography plays an important part in diagnostic aid in cardiac diseases. A critical step in echocardiography-aided diagnosis is to extract the standard planes since they tend to provide promising views to present different structures that are benefit to diagnosis. To this end, this paper proposes a spatial-temporal embedding framework to extract the standard view planes from 4D STIC (spatial-temporal image corre- lation) volumes. The proposed method is comprised of three stages, the frame smoothing, spatial-temporal embedding and final classification. In first stage, an L 0 smoothing fi…
CP-UNet: Contour-based Probabilistic Model for Medical Ultrasound Images Segmentation
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Tianjin Key Laboratory of Cognitive Computing and Application（天津认知计算与应用重点实验室）；Tianjin Key Laboratory of Advanced Networking（天津先进网络重点实验室）；School of Future Technology, Tianjin University（天津大学未来技术学院）
University of Michigan（密歇根大学）；University of California San Diego（加州大学圣迭戈分校）
For speech research, ultrasound tongue imaging provides a non-invasive means for visualizing tongue position and movement during articulation. Extracting tongue contours from ultrasound images is a basic step in analyzing ultrasound data but this task often requires non-trivial manual annotation. This study presents an open source tool for fully automatic tracking of tongue contours in ultrasound frames using neural network based methods. We have implemented and systematically compared two convolutional neural networks, U-Net and DenseU-Net, under different conditions. Though both models can p…
the Third Affiliated Hospital of Sun Yat-Sen University（中山大学附属第三医院）；School of Mathematical Sciences, Zhejiang University（浙江大学数学科学学院）；Henan Provincial People’s Hospital（河南省人民医院）
In this paper, a multi-scale framework with local region based active contour and boundary shape similarity constraint is proposed for the segmentation of levator hiatus in ultrasound images. In this paper, we proposed a multiscale active contour framework to segment levator hiatus ultrasound images by combining the local region information and boundary shape similarity constraint. In order to get more precisely initializations and reduce the computational cost, Gaussian pyramid method is used to decompose the image into coarse-to-fine scales. A localized region active contour model is firstly…
Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac structure and function. One disadvantage of CMR is that post-processing of exams is tedious. Without automation, precise assessment of cardiac function via CMR typically requires an annotator to spend tens of minutes per case manually contouring ventricular structures. Automatic contouring can lower the required time per patient by generating contour suggestions that can be lightly modified by the annotator. Fully convolutional networks (FCNs), a variant of convolutional neural networks, have been used to rapidly advan…
Local2Mesh: Spatially Localized Contour-to-Mesh for Left Ventricular Reconstruction from Sparse 2D Cardiac MRI
Department of Health Technology and Informatics, The Hong Kong Polytechnic University（香港理工大学健康科技与信息学系）；Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham（诺丁汉大学医学院诺丁汉生物医学研究中心）
With superiorities on low cost, portability, and free of radiation, echocardiogram is a widely used imaging modality for left ventricle (LV) function quantification. However, automatic LV segmentation and motion tracking is still a challenging task. In addition to fuzzy border definition, low contrast, and abounding artifacts on typical ultrasound images, the shape and size of the LV change significantly in a cardiac cycle. In this work, we propose a temporal affine network (TAN) to perform image analysis in a warped image space, where the shape and size variations due to the cardiac motion as…
Faculty of Engineering and Applied Sciences, Memorial University（纪念大学工程与应用科学学院）；Faculty of Medicine, Memorial University（纪念大学医学院）
Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions including sepsis, trauma, dialysis and congestive heart failure. Recent studies have shown that estimates of circulating blood volume can be obtained from the cross-sectional area (CSA) of the internal jugular vein (IJV) from ultrasound images. However, accurate segmentation and tracking of the IJV in ultrasound imaging is a challenging task and is significantly influenced by a number of parameters such as the image quality, shape, and temporal variation. In …
School of Electrical Engineering and Computer Science, University of Ottawa（渥太华大学电气工程与计算机科学学院）
The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation. Owing to the development of computational units, availability of digital datasets, and improved performance of deep learning models, fully automatic and accurate tracking of tongue contours in real-time ultrasound data became practical only in recent years. Recent studies have shown that the performance of deep learning techniques is significant in the tracking of ultrasound tongue contours in real-time applications such as…
Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering
Department of Biomedical Engineering, National University of Singapore（新加坡国立大学生物医学工程系）；School of Automation, Southeast University（东南大学自动化学院）；Department of Medicine, National University of Singapore（新加坡国立大学医学系）；Department of Cardiology, National University Heart Centre Singapore（新加坡国立大学心脏中心心脏病学系）
Inria（法国国家信息与自动化研究所）；IHU Liryc（利里克大学医院研究所）；University of Bordeaux（波尔多大学）
Radiological imaging offers effective measurement of anatomy, which is useful in disease diagnosis and assessment. Previous study has shown that the left atrial wall remodeling can provide information to predict treatment outcome in atrial fibrillation. Nevertheless, the segmentation of the left atrial structures from medical images is still very time-consuming. Current advances in neural network may help creating automatic segmentation models that reduce the workload for clinicians. In this preliminary study, we propose automated, two-stage, three-dimensional U-Nets with convolutional neural …
One usage of medical ultrasound imaging is to visualize and characterize human tongue shape and motion during a real-time speech to study healthy or impaired speech production. Due to the low-contrast characteristic and noisy nature of ultrasound images, it might require expertise for non-expert users to recognize tongue gestures in applications such as visual training of a second language. Moreover, quantitative analysis of tongue motion needs the tongue dorsum contour to be extracted, tracked, and visualized. Manual tongue contour extraction is a cumbersome, subjective, and error-prone task.…
In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Edge, texture, and intensity features are extracted from the first frame, and then mean shift algorithm is applied to each feature separately to find the center of ellipse related to that feature in the next frame. The center of ellipse will be the weighted average of these centers…
Carnegie Mellon University（卡内基梅隆大学）；Wake Forest Institute for Regenerative Medicine（维克森林再生医学研究所）
Ultra High Frequency Ultrasound (UHFUS) enables the visualization of highly deformable small and medium vessels in the hand. Intricate vessel-based measurements, such as intimal wall thickness and vessel wall compliance, require sub-millimeter vessel tracking between B-scans. Our fast GPU-based approach combines the advantages of local phase analysis, a distance-regularized level set, and an Extended Kalman Filter (EKF), to rapidly segment and track the deforming vessel contour. We validated on 35 UHFUS sequences of vessels in the hand, and we show the transferability of the approach to 5 more…
GE Vingmed Ultrasound, GE Healthcare（通用电气医疗集团GE Vingmed超声部门）；University of Oslo（奥斯陆大学）；Norwegian Computing Center（挪威计算中心）
Two-dimensional echocardiography (2DE) measurements of left ventricle (LV) dimensions are highly significant markers of several cardiovascular diseases. These measurements are often used in clinical care despite suffering from large variability between observers. This variability is due to the challenging nature of accurately finding the correct temporal and spatial location of measurement endpoints in ultrasound images. These images often contain fuzzy boundaries and varying reflection patterns between frames. In this work, we present a convolutional neural network (CNN) based approach to aut…
Imperial College London（伦敦帝国学院）；King’s College London（伦敦国王学院）；St Thomas’ Hospital（圣托马斯医院）
Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volume of screening cases, limits the practically achievable detection rates. In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound. We propose a pipeline for automated data curation and classification. During both training and inference, we exploit an auxiliary view classification task to bias features toward relevant cardiac stru…
Anatomical Prior-Driven Framework for Autonomous Robotic Cardiac Ultrasound Standard View Acquisition
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学智能制造装备与技术国家重点实验室）；School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）生物医学工程学院）；Information Intelligence Lab, Department of Electrical Engineering, National Chung Hsing University（中原大学电子工程系信息智能实验室）；Institute of Medical Equipment Science and Engineering, Huazhong University of Science and Technology（华中科技大学医学装备科学与工程研究院）；Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属同济医院超声医学科）；Institute of Systems Science (ISS), National University of Singapore (NUS)（新加坡国立大学系统科学研究所）
Ultromics Ltd.（奥特罗米克斯有限公司）；Metalynx Ltd.（梅塔林克斯有限公司）；Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）
Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep learning networks that are trained to predict disease or detect image features. However, such models are extremely data-hungry and training requires labelling of many thousands of images by experienced clinicians. Here we propose the use of contrastive learning to mitigate the labelling bottleneck. We train view classification models for imbalanced cardiac ultrasound datasets and show improved performance for views/cl…
Beijing University of Posts and Telecommunications（北京邮电大学）
Medical image segmentation is one of the important tasks of computer-aided diagnosis in medical image analysis. Since most medical images have the characteristics of blurred boundaries and uneven intensity distribution, through existing segmentation methods, the discontinuity within the target area and the discontinuity of the target boundary are likely to lead to rough or even erroneous boundary delineation. In this paper, we propose a new iterative refined interactive segmentation method for medical images based on agent reinforcement learning, which focuses on the problem of target segmenta…
Benchmarking Self-Supervised Models for Cardiac Ultrasound View Classification
Department of Systems；Computer Engineering, Carleton University, Ottawa, Ontario, Canada Department of Methodological；Implementation Research, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada Department of Acute Care Research, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada Department of Obstetrics；Gynecology, University of Ottawa, Ottawa, Ontario, Canada School of Epidemiology；Public Health, University of Ottawa, Ottawa, Ontario, Canada Department of Obstetrics, Gynecology \& Newborn Care, The Ottawa Hospital, Ottawa, Ontario, Canada International；Global Health Office, University of Ottawa, Ottawa, Ontario, Canada College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（医学超声关键技术研发实验室）；School of Biomedical Engineering, Medical School, Shenzhen University（生物医学工程学院）；Faculty of Applied Sciences, Macao Polytechnic University（应用科学学院）；Centre for CIMIM, Manchester University（CIMIM研究中心）；Center for Cardiovascular Disease（心血管疾病中心）；The Affiliated Suzhou Hospital of Nanjing Medical University（南京医科大学附属苏州医院）；Suzhou Municipal Hospital, Gusu School, Suzhou, China（苏州 municipal 医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；The First Affiliated Hospital of Anhui Medical University North（安徽医科大学北院）；The Second People’s Hospital（第二人民医院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；The People's Hospital of Tongren City（铜仁市人民医院）；The First People's Hospital of Taicang City（太仓市第一人民医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
King’s College London（伦敦国王学院）；Ultromics Ltd.（Ultromics有限公司）
Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients. Ultrasound imaging is the primary modality for cardiac imaging, however acquisition requires high operator skill, and its interpretation and analysis is difficult due to artifacts. Reconstructing cardiac anatomy in 3D can enable discovery of new biomarkers and make imaging less dependent on operator expertise, however most ultrasound systems only have 2D imaging capabilities. We propose both a simple alteration to the Pix2Vox++ networks fo…
Automated Interpretable 2D Video Extraction from 3D Echocardiography
University of California, Los Angeles（加州大学洛杉矶分校）；Kaiser Permanente Division of Research（凯撒医疗集团研究部）；Cedars-Sinai Medical Center（西达赛西医学中心）；Stanford University（斯坦福大学）
College of Science, Liaoning Petrochemical University（辽宁石油化工大学理学院）；Industrial Mathematics Laboratory, Baikal School of BRICS, Irkutsk National Research Technical University（伊尔库茨克国立研究技术大学贝加尔金砖学院工业数学实验室）
Biodegradable elastic scaffolds have attracted more and more attention in the field of soft tissue repair and tissue engineering. These scaffolds made of porous bioelastomers support tissue ingrowth along with their own degradation. It is necessary to develop a computer-aided analyzing method based on ultrasound images to identify the degradation performance of the scaffold, not only to obviate the need to do destructive testing, but also to monitor the scaffold's degradation and tissue ingrowth over time. It is difficult using a single traditional image processing algorithm to extract continu…
Compositional Segmentation of Cardiac Images Leveraging Metadata
School of Electronic Engineering and Computer Science, Queen Mary University of London（伦敦玛丽女王大学电子工程与计算机科学学院）；Queen Mary’s Digital Environment Research Institute (DERI)（玛丽女王数字环境研究所）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；School of Engineering and Materials Science, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院）
Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking
University of Sherbrooke（舍布鲁克大学）；INSA（法国国立应用科学学院）；Université Lyon 1（里昂第一大学）；CREATIS（CREATIS实验室）；Institut Universitaire de France (IUF)（法国大学研究院（IUF））；NTNU（挪威科技大学）；St. Olavs Hospital（圣奥拉夫医院）；SINTEF Digital（SINTEF数字研究所）；Levanger Hospital（莱旺厄尔医院）；Nord-Trøndelag Hospital Trust（北特伦德拉格医院信托）；Hôpital Croix-Rousse（克鲁瓦鲁斯医院）；Hospices Civils de Lyon（里昂民用医院集团）；Hôpital Lyon Sud（里昂南部医院）
