Echocardiography is a powerful prenatal examination tool for early diagnosis of fetal congenital heart diseases (CHDs). The four-chamber (FC) view is a crucial and easily accessible ultrasound (US) image among echocardiography images. Automatic analysis of FC views contributes significantly to the early diagnosis of CHDs. The first step to automatically analyze fetal FC views is locating the fetal four crucial chambers of heart in a US image. However, it is a greatly challenging task due to several key factors, such as numerous speckles in US images, the fetal cardiac chambers with small size and unfixed positions, and category indistinction caused by the similarity of cardiac chambers. These factors hinder the process of capturing robust and discriminative features, hence destroying fetal cardiac anatomical chambers precise localization. Therefore, we first propose a multistage residual hybrid attention module (MRHAM) to improve the feature learning. Then, we present an improved YOLOv4 detection model, namely MRHAM-YOLOv4-Slim. Specially, the residual identity mapping is replaced with the MRHAM in the backbone of MRHAM-YOLOv4-Slim, accurately locating the four important chambers in fetal FC views. Extensive experiments demonstrate that our proposed method outperforms current state-of-the-art, including the precision of 0.919, the recall of 0.971, the F1 score of 0.944, the mAP of 0.953, and the frames per second (FPS) of 43.
Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）；Nuffield Department of Women’s and Reproductive Health, University of Oxford（妇女与生殖健康纽菲尔德部门，牛津大学）
Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港脑心血管健康工程中心）；Department of Maternal and Child Health and Urological Sciences, Sapienza University of Rome（罗马萨皮恩扎大学妇幼保健与泌尿科学系）；Nuffield Department of Women’s and Reproductive Health, University of Oxford（牛津大学努尔菲尔德妇女与生殖健康系）
Institute of Biomedical Engineering, University of Oxford（牛津大学生物医学工程研究所）
Fetal heart motion is an important diagnostic indicator for structural detection and functional assessment of congenital heart disease. We propose an approach towards integrating deep convolutional and recurrent architectures that utilize localized spatial and temporal features of different anatomical substructures within a global spatiotemporal context for interpretation of fetal echocardiography videos. We formulate our task as a cardiac structure localization problem with convolutional architectures for aggregating global spatial context and detecting anatomical structures on spatial region…
IBM Research – Almaden Research Center（IBM阿尔马登研究中心）；Eastern Virginia Medical School（东弗吉尼亚医学院）
Congenital heart disease (CHD) is the most common congenital abnormality associated with birth defects in the United States. Despite training efforts and substantial advancement in ultrasound technology over the past years, CHD remains an abnormality that is frequently missed during prenatal ultrasonography. Therefore, computer-aided detection of CHD can play a critical role in prenatal care by improving screening and diagnosis. Since many CHDs involve structural abnormalities, automatic segmentation of anatomical structures is an important step in the analysis of fetal echocardiograms. While …
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.
Echocardiography to Cardiac MRI View Transformation for Real-Time Blind Restoration
Tampere University（坦佩雷大学）；Qatar University（卡塔尔大学）；VTT Technical Research Centre of Finland（芬兰国家技术研究中心）；Hamad Medical Corporation Hospital（哈马德医疗公司医院）；Hayatabad Medical Complex（海亚特阿巴德医疗中心）
DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI
King’s College London（伦敦国王学院）；Evelina London Children’s Hospital（伊芙琳娜伦敦儿童医院）；Imperial College London（帝国理工学院）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）
Peng Cheng Laboratory（鹏城实验室）；Beihang University（北京航空航天大学）；Capital Medical University（首都医科大学）；Beijing Anzhen Hospital（北京安贞医院）；Beijing Advanced Innovation Center for Big Data-Based Precision Medicine（北京大数据精准医疗先进创新中心）
As an important scan plane, four chamber view is routinely performed in both second trimester perinatal screening and fetal echocardiographic examinations. The biometrics in this plane including cardio-thoracic ratio (CTR) and cardiac axis are usually measured by sonographers for diagnosing congenital heart disease. However, due to the commonly existing artifacts like acoustic shadowing, the traditional manual measurements not only suffer from the low efficiency, but also with the inconsistent results depending on the operators' skills. In this paper, we present an anchor-free ellipse detectio…
The University of British Columbia（不列颠哥伦比亚大学）；Vancouver General Hospital（温哥华综合医院）
In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements. However, in emergencies or point-of-care situations, acquiring an ECG is often not an option, hence motivating the need for alternative temporal synchronization methods. Here, we propose Echo-SyncNet, a self-supervised learning framework to synchronize various cross-sectional 2D echo series without any external input. The proposed framework takes advantage of both intra-view and inter-view self supervisions. The former relies on spatiote…
King's College London（伦敦国王学院）；Guy’s and St Thomas' Hospital（盖伊和圣托马斯医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Royal Brompton Hospital (Guy’s and St Thomas' NHS Foundation Trust)（皇家布朗普顿医院（盖伊和圣托马斯NHS基金会信托））
Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function (LV ejection fraction) as a diagnostic and treatment stratification biomarker is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction. However, from time-varying 2-D echocardiography acquisition, a richer descr…
Cardiac valve event timing in echocardiography using deep learning and triplane recordings
GE Vingmed Ultrasound；Norwegian University of Science and Technology（挪威科技大学）；Oslo University Hospital, Rikshospitalet（奥斯陆大学医院（雷克沙医院））；University of Oslo（奥斯陆大学）；SINTEF Digital（SINTEF数字研究所）；St. Olavs hospital（圣奥拉夫斯医院）
The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning
King’s College London（伦敦国王学院）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（帝国理工学院）；Guy’s & St. Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；Evelina London Children’s Hospital（伊夫琳娜伦敦儿童医院）
A Novel Data Augmentation Tool for Enhancing Machine Learning Classification: A New Application of the Higher Order Dynamic Mode Decomposition for Improved Cardiac Disease Identification
Universidad Politécnica de Madrid（马德里理工大学）；ETSI Aeronáutica y del Espacio（航空航天高级技术工程学院）；ETSI Telecomunicación（电信高级技术工程学院）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Centro Nacional de Investigaciones Cardiovasculares (CNIC)（国家心血管研究中心）；Center for Computational Simulation (CCS)（计算仿真中心）
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）
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…
Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Universidad Politécnica de Madrid（马德里理工大学）；Centro Nacional de Investigaciones Cardiovasculares (CNIC)（国家心血管研究中心）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Center for Computational Simulation (CCS)（计算仿真中心）
University of Alberta（阿尔伯塔大学）；Mazankowski Alberta Heart Institute（马赞科夫斯基阿尔伯塔心脏研究所）
Purpose: Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is challenging due to the inherent properties of ultrasound imaging, such as the presence of speckle noise and the low signal-to-noise ratio. Methods: We propose a semi-automated segmentation algorithm for the delineation of the left ventricle in temporal 3D echocardiography sequences. The method requires minimal user interaction and relies on a diffeomorphic registration approach. Advantages of the method include no d…
We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart. Our method is able to jointly predict the visibility, viewing plane, location and orientation of the fetal heart at the frame level. The contributions of the paper are three-fold: (i) a convolutional neural network architecture is developed for a multi-task prediction, which is computed by sliding a 3x3 window spatially through convolutional maps. (ii) an anchor mechanism and Intersection over Union (IoU) loss are applied for i…
Continuous 3D Myocardial Motion Tracking via Echocardiography
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Medical School, Nanjing University（南京大学医学院）；Zhongshan Hospital, Fudan University（复旦大学中山医院）；College of Optical Sciences, University of Arizona（亚利桑那大学光学科学学院）
The University of Winnipeg（温尼伯大学）；Qatar University（卡塔尔大学）；Hamad Medical Corporation（哈马德医疗公司）
Cardiac imaging known as echocardiography is a non-invasive tool utilized to produce data including images and videos, which cardiologists use to diagnose cardiac abnormalities in general and myocardial infarction (MI) in particular. Echocardiography machines can deliver abundant amounts of data that need to be quickly analyzed by cardiologists to help them make a diagnosis and treat cardiac conditions. However, the acquired data quality varies depending on the acquisition conditions and the patient's responsiveness to the setup instructions. These constraints are challenging to doctors especi…
University of Sherbrooke（舍布鲁克大学）；Univ Lyon（里昂大学）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣艾蒂安大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；Institut Universitaire de France（法国大学研究院）
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, CNNs still struggle to leverage temporal information to provide accurate and temporally consistent segmentation maps across the whole cycle. Such consistency is required to accurately describe the cardiac function, a necessary step in diagnosing many cardiovascular diseases. In this paper, we propose a framework to learn the 2D+time apical long-axi…
EchoXFlow: A Beamspace Echocardiography Dataset for Cardiac Motion, Flow, and Function
Medical Technology & E-Health, Akershus University Hospital, Norway（医疗技术与电子健康，阿克ershus大学医院，挪威）；Department of Technology Systems, University of Oslo, Norway（技术系统系，奥斯陆大学，挪威）；Department of Cardiology, Akershus University Hospital, Norway（心脏病学系，阿克ershus大学医院，挪威）
Cardiovascular Research Institute, University of California, San Francisco（加州大学旧金山分校心血管研究所）；Department of Electrical Engineering and Computer Science, University of California at Berkeley（加州大学伯克利分校电气工程与计算机科学系）；Department of Medicine, University of California, San Francisco（加州大学旧金山分校医学系）；Division of Cardiology, Department of Medicine, and Feinberg Cardiovascular Research Institute（医学系心脏病学科及范伯格心血管研究所）
Automated cardiac image interpretation has the potential to transform clinical practice in multiple ways including enabling low-cost serial assessment of cardiac function in the primary care and rural setting. We hypothesized that advances in computer vision could enable building a fully automated, scalable analysis pipeline for echocardiogram (echo) interpretation. Our approach entailed: 1) preprocessing; 2) convolutional neural networks (CNN) for view identification, image segmentation, and phasing of the cardiac cycle; 3) quantification of chamber volumes and left ventricular mass; 4) parti…
University of California, San Francisco（加州大学旧金山分校）
Prenatal diagnosis of tetralogy of Fallot (TOF) and hypoplastic left heart syndrome (HLHS), two serious congenital heart defects, improves outcomes and can in some cases facilitate in utero interventions. In practice, however, the fetal diagnosis rate for these lesions is only 30-50 percent in community settings. Improving fetal diagnosis of congenital heart disease is therefore critical. Deep learning is a cutting-edge machine learning technique for finding patterns in images but has not yet been applied to prenatal diagnosis of congenital heart disease. Using 685 retrospectively collected ec…
Neural Implicit 3D Cardiac Shape Reconstruction from Sparse CT Angiography Slices Mimicking 2D Transthoracic Echocardiography Views
Department of Biomedical Engineering & Physics, Amsterdam UMC（阿姆斯特丹大学医学中心生物医学工程与物理系）；qurAI group, Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息研究所qurAI组）；Department of Radiology, Mayo Clinic（梅奥诊所放射科）；Department of Biomedical Signals and Systems, University of Twente（特温特大学生物医学信号与系统系）；Department of Cardiology, Amsterdam UMC（阿姆斯特丹大学医学中心心脏病科）；Department of Radiology & Nuclear Medicine, Amsterdam UMC（阿姆斯特丹大学医学中心放射与核医学科）
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
Cedars-Sinai Medical Center（西达赛奈医疗中心）；University of California Los Angeles（加州大学洛杉矶分校）；Stanford University（斯坦福大学）；University of California, San Francisco（加州大学旧金山分校）；San Francisco Veterans Affairs Medical Center（旧金山退伍军人事务医疗中心）
GDKVM: Echocardiography Video Segmentation via Spatiotemporal Key-Value Memory with Gated Delta Rule
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University（香港理工大学智能健康中心）
Congenital heart disease is considered as one the most common groups of congenital malformations which affects $6-11$ per $1000$ newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of congenital heart disease. We propose an unsupervised approach that learns healthy anatomy exclusively from clinically confirmed normal control patients. We evaluate a number of known anomaly detection frameworks together with a model architecture based on the…
Echo4DIR: 4D Implicit Heart Reconstruction from 2D Echocardiography Videos
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；School of Information Science and Engineering, Yunnan University, Kunming, China（云南大学信息科学与工程学院）
EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography
Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属上海儿童医学中心）；Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP)（上海智能儿科工程技术研究中心）；East China Normal University（华东师范大学）；University of Shanghai for Science and Technology（上海理工大学）
Unsupervised 4D Cardiac Motion Tracking with Spatiotemporal Optical Flow Networks
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；DJI Technology Co., Ltd.（大疆创新科技有限公司）；Shenzhen University of Advanced Technology（深圳理工大学）
Echocardiography Segmentation Using Neural ODE-based Diffeomorphic Registration Field
University of Engineering and Technology, Vietnam National University（越南国立大学工程技术大学）；VinUniversity（文梅大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
NVIDIA（英伟达）；Lenovo Research（联想研究院）；Piedmont Heart Institute（皮埃蒙特心脏研究所）；Tencent Medical AI Lab（腾讯医疗人工智能实验室）；University of California, Irvine（加州大学欧文分校）
Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow from echocardiogram is time-consuming and error-prone. Computer algorithms that can automatically track and quantify myocardial motion and cardiac blood flow are highly sought after, but have not been very successful due to noise and high variability of echocardiography. In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense track…
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…
DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View
School of Information and Communication（信息与通信学院）；Guilin University of Electronic Technology（桂林电子科技大学）；Faculty of Humanities and Arts（人文艺术学院）；Macau University of Science and Technology（澳门科技大学）；Guangxi Key Laboratory of Machine Vision and Intelligent Control（广西机器视觉与智能控制重点实验室）；Wuzhou University（梧州大学）；Faculty of Innovation Engineering（创新工程学院）；Department of Public Education（公共教育系）；Wuzhou Medical College（梧州医学院）；College of Big Data and Software Engineering（大数据与软件工程学院）；Shenzhen Maternity and Child Healthcare Hospital（深圳妇幼保健医院）；Southern Medical University（南方医科大学）；School of Microelectronics（微电子学院）
