We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the worldwide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal. The network termed US-GuideNet predicts either the movement towards the standard plane position (goal prediction), or the next movement that an expert sonographer would perform (action prediction). While existing models for other ultrasound applications are trained with simulations or phantoms, we train our model with real-world ultrasound video and probe motion data from 464 routine clinical scans by 17 accredited sonographers. Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88.8% for goal prediction and 90.9% for action prediction.
University of Oxford（牛津大学）；Leiden University Medical Center（莱顿大学医学中心）；Shanghai Jiao Tong University（上海交通大学）
Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in freehand scanning, but challenging. We propose AdLocUI, a framework that Adaptively Localizes 2D Ultrasound Images in the 3D anatomical atlas without using any external tracking sensor.. We first train a convolutional neural network with 2D slices sampled from co-aligned 3D ultrasound volumes to predict their locations in the 3D anatomical atlas. Next, we fine-tune …
EnCoV-ISIT UMR 6284 CNRS/Université d’Auvergne（法国国家科学研究中心/奥弗涅大学 EnCoV-ISIT 联合研究单位（UMR 6284））
The aim of this work is to implement a simple freehand ultrasound (US) probe calibration technique. This will enable us to visualize US image data during surgical procedures using augmented reality. The performance of the system was evaluated with different experiments using two different pose estimation techniques. A near-millimeter accuracy can be achieved with the proposed approach. The developed system is cost-effective, simple and rapid with low calibration error
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）
Rensselaer Polytechnic Institute（伦斯勒理工学院）；National Institutes of Health（美国国立卫生研究院）
Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information. Current methods for 3D volume reconstruction from freehand US scans require external tracking devices to provide spatial position for every frame. In this paper, we propose a deep contextual learning network (DCL-Net), which can efficiently exploit the image feature relationship between US frames and reconstruct 3D US volumes without any tracking device. The proposed DCL-Net utilizes 3D convolutions over a US video segment for feature extra…
Ultrasound imaging is the most popular medical imaging modality for point-of-care bedside imaging. However, 2D ultrasound imaging provides only limited views of the organ of interest, making diagnosis challenging. To overcome this, 3D ultrasound imaging was developed, which uses 2D ultrasound images and their orientation/position to reconstruct 3D volumes. The accurate position estimation of the ultrasound probe at low cost has always stood as a challenging task in 3D reconstruction. In this study, we propose a novel approach of using a mechanical track for ultrasound scanning, which restricts…
Freehand 3D ultrasound (US) has important clinical value due to its low cost and unrestricted field of view. Recently deep learning algorithms have removed its dependence on bulky and expensive external positioning devices. However, improving reconstruction accuracy is still hampered by difficult elevational displacement estimation and large cumulative drift. In this context, we propose a novel deep motion network (MoNet) that integrates images and a lightweight sensor known as the inertial measurement unit (IMU) from a velocity perspective to alleviate the obstacles mentioned above. Our contr…
Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Technical University of Denmark（技术大学）；Pioneer Centre for AI（先锋人工智能中心）；University of Copenhagen（哥本哈根大学）；Slagelse Hospital（斯拉格尔塞医院）；CAMES Rigshospitalet（CAMES皇家医院）
Pose-GuideNet: Automatic Scanning Guidance for Fetal Head Ultrasound from Pose Estimation
Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）；Nuffield Department of Women’s & Reproductive Health, University of Oxford（牛津大学纳菲尔德妇女与生殖健康系）
Shenzhen University（深圳大学）；Marshall Laboratory of Biomedical Engineering（马歇尔生物医学工程实验室）
3D ultrasound (US) is widely used for its rich diagnostic information. However, it is criticized for its limited field of view. 3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. The existing deep learning based methods only focus on the basic cases of skill sequences, and the model relies on the training data heavily. The sequences in real clinical practice are a mix of diverse skills and have complex scanning paths. Besides, deep models should adapt themselves to the testing cases with prior knowledge for better robustness, rather…
Nonrigid Reconstruction of Freehand Ultrasound without a Tracker
Centre for Medical Image Computing（医学影像计算中心）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences（惠康/工程与物理科学研究理事会介入与外科科学中心）；Department of Medical Physics and Biomedical Engineering（医学物理与生物医学工程系）；University College London（伦敦大学学院）；School of Biomedical Engineering & Imaging Sciences（生物医学工程与影像科学学院）；King’s College London（伦敦国王学院）
MoNetV2: Enhanced Motion Network for Freehand 3D Ultrasound Reconstruction
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China（国家级医学超声关键技术研发实验室、生物医学工程学院、深圳大学医学院、深圳大学、深圳、广东、中国）；Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, Guangdong, China（医学超声图像计算（MUSIC）实验室、深圳大学、深圳、广东、中国）；Shenzhen RayShape Medical Technology Inc.（深圳RayShape医疗科技有限公司）；Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China（肿瘤中心、超声医学科、浙江省人民医院、杭州医学院附属人民医院、杭州、浙江、中国）；Department of Health Management Center, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China（健康管理中心、齐鲁医院、山东大学齐鲁医学院、济南、山东、中国）
TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker
organization= UCL Hawkes Institute, Department of Medical Physics；Biomedical Engineering, University College London , city= London , postcode= WC1E 6BT , country= U.K.；organization= Centre for Bioengineering, Digital Environment Research Institute, School of Engineering；Materials Science, Queen Mary University of London , city= London , postcode= E1 4NS , country= U.K.；organization= National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University , city= Shenzhen , postcode= 518000 , country= China；organization= School of Artificial Intelligence, Shenzhen University , city= Shenzhen , postcode= 518060 , country= China；organization= Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University , city= Shenzhen , postcode= 518000 , country= China；organization= Marshall Laboratory of Biomedical Engineering, Shenzhen University , city= Shenzhen , postcode= 518000 , country= China；organization= Shenzhen RayShape Medical Technology Inc. , city= Shenzhen , postcode= 518000 , country= China；organization= DKFZ (German Cancer Research Center) Heidelberg , country= Germany；organization= University of Cincinnati, Cincinnati, OH, 45219 , country= USA；organization= Tufts University , country= USA；organization= Hong Kong Centre for Cerebro-cardiovascular Health Engineering, Rm 1115-1119, Building 19W, Hong Kong Science Park , city= Hong Kong SAR , country= China；organization= City University of Hong Kong, Kowloon , city= Hong Kong SAR , country= China；organization= Department of Information Convergence Engineering, Pusan National University , city= Yangsan , country= Korea；organization= School of Biomedical Convergence Engineering, Pusan National University , city= Yangsan , country= Korea；organization= Center for Artificial Intelligence Research, Pusan National University , city= Busan , country= Korea；organization= Department of Medical Physics, University of Wisconsin (UW) School of Medicine；organization= Department of Electrical；organization= Department of Biostatistics；Medical Informatics, UW School of Medicine；organization= Department of Computer Sciences, UW–Madison , city= Madison , postcode= WI 53706 , country= USA；organization= School of Biomedical Engineering \& Imaging Sciences, King’s College London , city= London , postcode= WC2R 2LS , country= U.K.
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
Jinan University（暨南大学）；The University of Auckland（奥克兰大学）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；The Hong Kong University of Science and Technology（香港科技大学）；University of Girona（赫罗纳大学）；Canon Medical Systems (China) Co., LTD（佳能医疗系统（中国）有限公司）；Nankai University（南开大学）；Hunan University（湖南大学）；University College Dublin（都柏林大学学院）；Zhejiang Gongshang University（浙江工商大学）；Chongqing University of Technology（重庆理工大学）；Shenzhen University（深圳大学）；NanFang Hospital of Southern Medical University（南方医科大学南方医院）；Zhujiang Hospital of Southern Medical University（南方医科大学珠江医院）；Universitat de Barcelona（巴塞罗那大学）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚研究与高级研究机构）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Samsung Medison Co., Ltd.（三星麦迪逊有限公司）；Chung-Ang University Hospital（中央大学医院）
In contrast to 2-D ultrasound (US) for uniaxial plane imaging, a 3-D US imaging system can visualize a volume along three axial planes. This allows for a full view of the anatomy, which is useful for gynecological (GYN) and obstetrical (OB) applications. Unfortunately, the 3-D US has an inherent limitation in resolution compared to the 2-D US. In the case of 3-D US with a 3-D mechanical probe, for example, the image quality is comparable along the beam direction, but significant deterioration in image quality is often observed in the other two axial image planes. To address this, here we propo…
Institute of Research and Technology b<>com（b<>com技术研究院）
Purpose. Ability to locate and track ultrasound images in the 3D operating space is of great benefit for multiple clinical applications. This is often accomplished by tracking the probe using a precise but expensive optical or electromagnetic tracking system. Our goal is to develop a simple and low cost augmented reality echography framework using a standard RGB-D Camera. Methods. A prototype system consisting of an Occipital Structure Core RGB-D camera, a specifically-designed 3D marker, and a fast point cloud registration algorithm FaVoR was developed and evaluated on an Ultrasonix ultrasoun…
Efficient Feature Extraction Using Light-Weight CNN Attention-Based Deep Learning Architectures for Ultrasound Fetal Plane Classification
Amrita School of Artificial Intelligence（阿米特学校人工智能学院）；Amrita Vishwa Vidyapeetham（阿米特世界大学）；Center for Artificial Intelligence（人工智能中心）；Prince Mohammed Bin Fahd University（普里姆·穆罕默德·本·法赫德大学）
Self-Supervised Ultrasound Representation Learning for Renal Anomaly Prediction in Prenatal Imaging
organization= Department of Systems；Computer Engineering, Carleton University , city= Ottawa , state= Ontario , country= Canada；organization= Department of Methodological；Implementation Research, Ottawa Hospital Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Department of Acute Care Research, Ottawa Hospital Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Children's Hospital of Eastern Ontario Research Institute , city= Ottawa , state= Ontario , country= Canada；organization= Better Outcomes Registry \& Network Ontario, Children’s Hospital of Eastern , city= Ottawa , state= Ontario , country= Canada；organization= Department of Obstetrics；Gynecology, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= School of Epidemiology；Public Health, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= Department of Obstetrics, Gynecology \& Newborn Care, The Ottawa Hospital , city= Ottawa , state= Ontario , country= Canada；Global Health Office, University of Ottawa , city= Ottawa , state= Ontario , country= Canada；organization= Department of Clinical Science；Translational Medicine, University of Ottawa , city= Ottawa , state= Ontario , country= Canada
Technical University of Munich（慕尼黑工业大学）；Shanghai Jiao Tong University（上海交通大学）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；rechts der Isar University Hospital（伊萨尔河右岸大学医院）
Ultrasound (US) is one of the most common medical imaging modalities since it is radiation-free, low-cost, and real-time. In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. However, reproducibility and stability of the resulting images often suffer from intra- and inter-operator variation. Reinforcement learning (RL), as an interaction-based learning method, has demonstrated its effectiveness in visual navigating tasks; however, RL is limited in terms of generalization. To address this challenge, we pro…
