Match4Annotate: Cross-Video Annotation Transfer in Ultrasound via Implicit Feature Flow-Guided Matching
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, USA（麻省理工学院机械工程系）；Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, USA（麻省理工学院医学工程与科学研究所）；MIT.nano Immersion Lab, Massachusetts Institute of Technology, Cambridge, USA（麻省理工学院MIT.nano沉浸实验室）
Zhuorui Zhang, Roger Pallarès-López, Praneeth Namburi, Brian W. Anthony
Acquiring per-frame annotations for ultrasound videos is costly and requires clinical expertise, limiting learning-based analysis. We study cross-video annotation transfer: propagating user-specified annotations from a labeled ultrasound video to an independently acquired target video with no target-side labels or manual initialization. Video trackers and segmentation propagators rely on temporal continuity and require a prompt in every new sequence, whereas cross-image feature matching and one-shot segmentation estimate correspondences independently, without enforcing coherent deformations or supporting both point and mask annotations. We present Match4Annotate, a test-time framework with three stages. A spatiotemporal implicit feature representation lifts frozen vision foundation-model features into a continuous field over space and time, enabling queries beyond the backbone resolution. A continuous implicit feature flow then aligns the source and target fields under a smooth-deformation prior, estimating correspondence in feature space rather than relying on intensity consistency, which is often violated in ultrasound by speckle and acquisition-dependent appearance. Finally, flow-guided annotation transfer uses the estimated flow as a spatial prior over feature similarity. This formulation unifies sparse point and dense mask transfer and includes unconstrained feature matching and direct flow warping as limiting cases. On four clinical ultrasound datasets spanning echocardiography and musculoskeletal imaging, Match4Annotate achieves state-of-the-art annotation transfer, outperforming dense feature-matching baselines across PCK thresholds and one-shot segmentation methods in Dice score. It also demonstrates bidirectional transfer of left-ventricular annotations across datasets. It requires no task-specific training and adapts to each video in minutes on a single consumer GPU.
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；University of Copenhagen（哥本哈根大学）；Copenhagen Academy of Medical Education and Simulation (CAMES), Rigshospitalet（哥本哈根医学教育与模拟学院（CAMES），国立医院（Rigshospitalet））；Pioneer Centre for AI, Copenhagen（哥本哈根人工智能先锋中心）
Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件学院）；Shaanxi Joint Key Laboratory for Artifact Intelligence（陕西省人工智能联合重点实验室）；School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电气工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of General Surgery, Xiangya Hospital, Central South University（中南大学湘雅医院普外科）；Department of Orthopaedics, Second Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第二附属医院骨科）；Hunan Frontline Medical Technology Co., Ltd.（湖南前沿医疗科技有限公司）
ImplicitCell: Resolution Cell Modeling of Joint Implicit Volume Reconstruction and Pose Refinement in Freehand 3D Ultrasound
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Tongren Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属同仁医院）
Technische Universität München（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Trinity College Dublin（都柏林圣三一学院）
Intra-operative ultrasound is an increasingly important imaging modality in neurosurgery. However, manual interaction with imaging data during the procedures, for example to select landmarks or perform segmentation, is difficult and can be time consuming. Yet, as registration to other imaging modalities is required in most cases, some annotation is necessary. We propose a segmentation method based on DeepVNet and specifically evaluate the integration of pre-training with simulated ultrasound sweeps to improve automatic segmentation and enable a fully automatic initialization of registration. I…
University of Oxford（牛津大学）；Leiden University Medical Center（莱顿大学医学中心）
The objective of this work is to achieve sensorless reconstruction of a 3D volume from a set of 2D freehand ultrasound images with deep implicit representation. In contrast to the conventional way that represents a 3D volume as a discrete voxel grid, we do so by parameterizing it as the zero level-set of a continuous function, i.e. implicitly representing the 3D volume as a mapping from the spatial coordinates to the corresponding intensity values. Our proposed model, termed as ImplicitVol, takes a set of 2D scans and their estimated locations in 3D as input, jointly refining the estimated 3D …
In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotations. In this paper, we propose to address the problem of self-supervised representation learning with multi-modal ultrasound video-speech raw data. For this case, we assume that there is a high correlation between the ultrasound video and the corresponding narrative speech audio of the sonographer. I…
Low-Rank-Modulated Functa: Exploring the Latent Space of Implicit Neural Representations for Interpretable Ultrasound Video Analysis
Yale Biomedical Imaging Institute, Yale University（耶鲁大学生物医学成像研究所）；Department of Emergency Medicine, Yale School of Medicine（耶鲁大学医学院急诊医学系）；Dept. of Biomedical Engineering, University of Basel（巴塞尔大学生物医学工程系）
Eindhoven University of Technology（埃因霍温理工大学）；Shandong University of Science and Technology（山东科技大学）；Philips Research（飞利浦研究院）
Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for cardiac intervention. Currently, the state-of-the-art segmentation algorithms are based on convolutional neural networks (CNNs), which achieved remarkable performances in a standard Cartesian volumetric data. Nevertheless, these approaches suffer the challenges of low efficiency and GPU unfriendly image size. Therefore, such difficulties and expensive hardware requirements become a bottleneck to build accurate and efficient segmentation models for real clinical application. In this paper, we propose a novel Fru…
Department of Mechanical Engineering, MIT（麻省理工学院机械工程系）；Institute for Medical Engineering and Science, MIT（麻省理工学院医学工程与科学研究所）；MIT.nano Immersion Lab, MIT（麻省理工学院MIT.nano沉浸实验室）
Concordia University（康考迪亚大学）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Western University（韦仕敦大学）；St. Olavs Hospital（圣奥拉夫医院）；Trondheim University Hospital（特隆赫姆大学医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；SINTEF Digital（SINTEF数字研究所）
Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images play an essential role in surgical planning and resection of brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique annotation dataset of tumor tissues and resection cavities from the previously published RESECT dataset (Xiao et al. 2017) to encourage a more rigorous assessments of image processing techniques. Acquisition and validation methods: The RESECT database …
Chair for Computer Aided Medical Procedures (CAMP) Technical University of Munich（计算机辅助医疗程序研究所（CAMP）慕尼黑技术大学）；Robotics Institute Carnegie Mellon University（卡内基梅隆大学机器人研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper we propose a weakly supervised method for automatic confidence estimatio…
Uncertainty-Aware Extreme Point Tracing for Weakly Supervised Ultrasound Image Segmentation
College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）；Baotou Medical College（包头医学院）；Department of Ultrasound Medicine, The Second Affiliated Hospital of Baotou Medical College（包头医学院第二附属医院超声医学科）
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）
Beyond Point Annotation: A Weakly Supervised Network Guided by Multi-Level Labels Generated from Four-Point Annotation for Thyroid Nodule Segmentation in Ultrasound Image
Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China（机器人科学与工程学院，东北大学，沈阳，中国）；Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada（生物医学工程系，萨斯喀彻温大学，萨斯喀彻温，加拿大）；Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China（超声科，中国医科大学附属盛京医院，沈阳，中国）
OSCAR: Occupancy-based Shape Completion via Acoustic Neural Implicit Representations
Chair for Computer Aided Medical Procedures (CAMP) Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序教席（CAMP））；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；Konrad Zuse School of Excellence in Reliable AI (relAI)（康拉德·楚泽可靠人工智能卓越学校（relAI））
Ultrasound, because of its low cost, non-ionizing, and non-invasive characteristics, has established itself as a cornerstone radiological examination. Research on ultrasound applications has also expanded, especially with image analysis with machine learning. However, ultrasound data are frequently restricted to closed data sets, with only a few openly available. Despite being a frequently examined organ, the kidney lacks a publicly available ultrasonography data set. The proposed Open Kidney Ultrasound Data Set is the first publicly available set of kidney brightness mode (B-mode) ultrasound …
Learning-based Hierarchical Tracheal Anatomy Understanding from Sparse Surgical Demonstration Annotations for Ultrasound Robots
The Chinese University of Hong Kong（香港中文大学）；Imperial College London（伦敦帝国学院）；Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）；SLAI Shenzhen Loop Area Institute（深圳环域先进人工智能研究院）
Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonance imaging tumor annotations
Department of Health Research（健康研究系）；SINTEF Digital（SINTEF数字）；Department of Physics（物理系）；Norwegian University of Science and Technology (NTNU)（挪威科学与技术大学（NTNU））；Department of Circulation and Medical Imaging（循环与医学成像系）；St. Olavs hospital（圣奥拉夫医院）；Department of Neuromedicine and Movement Science（神经医学与运动科学系）
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures. In this work, a multi-feature guided convolutional neural network (CNN) architecture is proposed for simultaneous enhancement, segmentation, and classification of bone surfaces from US data. The proposed CNN consists of two main parts: a pre-enhancing net, that takes the concatenation of B-mode US scan and three filtered image features for the enhancement of bone surfaces, and a mo…
Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications. Therefore, there is significant interest in learning representations from unlabelled raw data. In this paper, we propose a self-supervised learning approach to learn meaningful and transferable representations from medical imaging video without any type of human annotation. We assume that in …
Korea University（高丽大学）；Hankik University of Foreign Studies（韩国外国语大学）
We propose a method for effectively utilizing weakly annotated image data in an object detection tasks of breast ultrasound images. Given the problem setting where a small, strongly annotated dataset and a large, weakly annotated dataset with no bounding box information are available, training an object detection model becomes a non-trivial problem. We suggest a controlled weight for handling the effect of weakly annotated images in a two stage object detection model. We~also present a subsequent active learning scheme for safely assigning weakly annotated images a strong annotation using the …
Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering
All India Institute of Medical Sciences (AIIMS)（全印医学科学学院）；Purdue University (PU)（普渡大学）；Indian Institute of Technology (IIT)（印度理工学院）；Berea College（伯里亚学院）
Multistep Automated Data Labelling Procedure (MADLaP) for Thyroid Nodules on Ultrasound: An Artificial Intelligence Approach for Automating Image Annotation
Duke University（杜克大学）；Duke University Medical Center（杜克大学医学中心）
Tencent YouTu X-Lab（腾讯优图X实验室）；Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；Peking University Shenzhen Hospital（北京大学深圳医院）
Breast lesion detection in ultrasound video is critical for computer-aided diagnosis. However, detecting lesion in video is quite challenging due to the blurred lesion boundary, high similarity to soft tissue and lack of video annotations. In this paper, we propose a semi-supervised breast lesion detection method based on temporal coherence which can detect the lesion more accurately. We aggregate features extracted from the historical key frames with adaptive key-frame scheduling strategy. Our proposed method accomplishes the unlabeled videos detection task by leveraging the supervision infor…
Shenzhen University（深圳大学）；Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University（中山大学孙逸仙纪念医院）
Breast cancer is one of the leading causes of cancer deaths in women. As the primary output of breast screening, breast ultrasound (US) video contains exclusive dynamic information for cancer diagnosis. However, training models for video analysis is non-trivial as it requires a voluminous dataset which is also expensive to annotate. Furthermore, the diagnosis of breast lesion faces unique challenges such as inter-class similarity and intra-class variation. In this paper, we propose a pioneering approach that directly utilizes US videos in computer-aided breast cancer diagnosis. It leverages ma…
Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videos
Shenzhen University（深圳大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳瑞影医疗科技有限公司）；The Third Affiliated Hospital, Sun Yat-sen University（中山大学附属第三医院）
Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong（香港中文大学深圳大数据研究院）；Huazhong University of Science and Technology（华中科技大学）；Shenzhen Third People’s Hospital（深圳市第三人民医院）
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD). Due to the noisy data, expensive annotations of US images, the application of Artificial Intelligence (AI) assisting approaches encounters a bottleneck. Besides, the use of mono-modal US data limits the further improve of the classification results. In this work, we innovatively propose a multi-modal fusion network with active learning (MMFN-AL) for ALFD to exploit the information of multiple modalities, eliminate the noisy data and red…
MambaXCTrack: Mamba-based Tracker with SSM Cross-correlation and Motion Prompt for Ultrasound Needle Tracking
The Chinese University of Hong Kong（香港中文大学）；State Grid Xin Jiang Electric Power Co., Ltd. Supervoltage Branch（国家电网新疆电力有限公司超高压分公司）；Duke Kunshan University（昆山杜克大学）
ADAptation: Reconstruction-based Unsupervised Active Learning for Breast Ultrasound Diagnosis
Faculty of Applied Sciences, Macao Polytechnic University, Macau, China（澳门理工大学应用科学学院）；Medical Ultrasound Image Computing (MUSIC) Lab, School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China（深圳大学生物医学工程学院）；Netherlands Cancer Institute, Amsterdam, Netherlands（荷兰癌症研究所）；Department of Ultrasound, Peking University Third Hospital, Beijing, China（北京第三人民医院超声科）
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…
Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究机器学习研究中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）；School of Computer Science and Digital Technologies, Aston University（阿斯顿大学计算机科学与数字技术学院）
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）深圳大数据研究院）
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for lung's multi-symptom (multi-label) classification. To overcome these difficulties, we propose a novel semi-supervised Two-Stream Active Learning (TSAL) method to model complicated features and reduce labeling costs in an iterative procedure. The core component of TSAL is the multi-label learning mechanism, in which lab…
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound
Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University（深圳大学医学部生物医学工程学院）；Shenzhen University（深圳大学）
MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentation
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University（上海大学先进通信与数据科学研究院智能医学与AI放射技术实验室）；School of Health Science and Engineering, University of Shanghai for Science and Technology（上海科技大学健康科学与工程学院）；Department of Pediatric Cardiology, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院儿童心脏科部）；School of Medical Instruments, Shanghai University of Medicine and Health Sciences（医学仪器学院，上海医学院与健康科学大学）
RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans
University of Oxford（牛津大学）；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）；Oxford Machine Learning in NeuroImaging Lab, University of Oxford（牛津大学神经影像机器学习实验室）
