Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images. Anatomy classification in fetal 2D ultrasound is an ideal problem setting to test whether these results translate to non-ideal data. Our results indicate that inclusion of a challenging background class can be detrimental and that semi-supervised learning mostly benefits classes that are already distinct, sometimes at the expense of more similar classes.
Determining Fetal Orientations From Blind Sweep Ultrasound Video
Technical University of Denmark（丹麦技术大学）；University of Copenhagen（哥本哈根大学）；Slagelse Hospital（斯劳厄尔瑟医院）；CAMES Rigshospitalet（CAMES 哥本哈根大学医院）；Pioneer Centre for AI（人工智能先锋中心）
This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i.e. without any localisation or segmentation information. We examine the use of convolutional neural network architectures coupled with soft proposal layers. The resulting network simultaneously performs anatomical region detection (classification) and localisation tasks. We generate a proposal map describing the attention of the network for a particular class. The network is trained on 85,500 2D fetal Ultrasound images and their as…
Learning to learn skill assessment for fetal ultrasound scanning
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）；Department of Medical Physics and Biomedical Engineering, University College London（医学物理与生物医学工程系，伦敦大学学院）；Nuffield Department of Women’s & Reproductive Health, University of Oxford（妇女与生殖健康系，牛津大学）；Rabin-Beilinson Medical Center, Tel-Aviv University Faculty of Medicine（拉宾-贝利松医疗中心，特拉维夫大学医学院）
Ultrasound (US) imaging is highly effective with regards to both cost and versatility in real-time diagnosis; however, determination of fetal gender by US scan in the early stages of pregnancy is also a cause of sex-selective abortion. This work proposes a deep learning object detection approach to accurately mask fetal gender in US images in order to increase the accessibility of the technology. We demonstrate how the YOLOv5L architecture exhibits superior performance relative to other object detection models on this task. Our model achieves 45.8% AP[0.5:0.95], 92% F1-score and 0.006 False Po…
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）
Towards Objective Obstetric Ultrasound Assessment: Contrastive Representation Learning for Fetal Movement Detection
Department of Electrical and Computer Systems Engineering, Monash University（莫纳什大学电气与计算机系统工程系）；AIM for Health Lab, Faculty of Information Technology, Monash University（健康AIM实验室，信息技术学院，莫纳什大学）；School of Biomedical Engineering, University of Sydney（悉尼大学生物医学工程学院）；Department of Chemical & Biological Engineering, Monash University（莫纳什大学化学与生物工程系）；Department of Obstetrics & Gynaecology, Monash University（莫纳什大学妇产科系）
Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as we…
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）
Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutiona…
FetalCLIP: A Visual-Language Foundation Model for Fetal Ultrasound Image Analysis
Department of Computer Vision（计算机视觉系）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫比兹人工智能大学）；Department of Machine Learning（机器学习系）；Corniche Hospital, Abu Dhabi Health Services Company (SEHA)（阿布扎赫尔医院，阿布扎赫健康服务公司（SEHA））
Sano Centre for Computational Medicine（萨诺计算医学中心）；Warsaw University of Technology（华沙理工大学）；Fetai Health Ltd.（Fetai健康有限公司）；Medical University of Warsaw（华沙医科大学）；Tooploox（Tooploox公司）
In this paper, we propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for spatio-temporal fetal ultrasound scan video analysis. Fetal biometric measurement is a standard examination during pregnancy used for the fetus growth monitoring and estimation of gestational age and fetal weight. The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur. Due to natural high speckle noise and shadows in ultrasound data, medical expertise and sonographic experience are requ…
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…
Multi-Contrast Fusion Module: An attention mechanism integrating multi-contrast features for fetal torso plane classification
School of Mathematics and Statistics, Xiamen University of Technology（厦门理工学院数学与统计学学院）；School of Computer and Information Engineering, Xiamen University of Technology（厦门理工学院计算机与信息工程学院）；Department of Ultrasound Imaging, The Second Affiliated Hospital of Xiamen Medical College（厦门医学院第二附属医院超声影像科）
Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound
Technical University of Denmark（丹麦技术大学）；Pioneer Centre for AI（先锋人工智能中心）；Technical University of Munich（慕尼黑技术大学）；Helmholtz AI and Helmholtz Munich（海德堡人工智能与海德堡慕尼黑）；King’s College London（伦敦国王学院）；University of Copenhagen（哥本哈根大学）；CAMES Rigshospitalet（哥本哈根大学医院）
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.
Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Research Ireland Centre for Research Training in Machine Learning（爱尔兰研究培训机器学习研究中心）；School of Medicine, University College Dublin（都柏林大学医学院）；School of Veterinary Medicine, University College Dublin（都柏林大学兽医学院）；School of Computer Science, University College Dublin（都柏林大学计算机科学学院）
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…
Imperial College London（帝国理工学院）；ETH Zurich（苏黎世联邦理工学院）；Guy’s and St Thomas’ NHS Foundation（盖伊和圣托马斯国民保健服务基金会）
Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine …
FetSelect: Task-Specific Architectures and Self-Supervised Learning for Automated Fetal Ultrasound Frame Selection
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）；LIST Laboratory Department of Electrical Systems Engineering, University of Boumerdes（布迈德斯大学电气系统工程系LIST实验室）；Sidra Medicine（锡德拉医学）
Technical University of Denmark（丹麦技术大学）；CAMES Rigshospitalet（哥本哈根大学医院CAMES中心）
Confounding information in the form of text or markings embedded in medical images can severely affect the training of diagnostic deep learning algorithms. However, data collected for clinical purposes often have such markings embedded in them. In dermatology, known examples include drawings or rulers that are overrepresented in images of malignant lesions. In this paper, we encounter text and calipers placed on the images found in national databases containing fetal screening ultrasound scans, which correlate with standard planes to be predicted. In order to utilize the vast amounts of data a…
One of the routine examinations that are used for prenatal care in many countries is ultrasound imaging. This procedure provides various information about fetus health and development, the progress of the pregnancy and, the baby's due date. Some of the biometric parameters of the fetus, like fetal head circumference (HC), must be measured to check the fetus's health and growth. In this paper, we investigated the effects of using multi-scale inputs in the network. We also propose a light convolutional neural network for automatic HC measurement. Experimental results on an ultrasound dataset of …
National Institute for Mathematical Sciences（国家数学科学研究所）；Yonsei University（延世大学）；Yonsei University College of Medicine（延世大学医学院）
Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient-specific, operator-dependent, and machine-specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, non-uniform contrast, and irregular shape compared to other paramet…
University of Oxford（牛津大学）；Tel-Aviv University（特拉维夫大学）
Self-supervised contrastive representation learning offers the advantage of learning meaningful visual representations from unlabeled medical datasets for transfer learning. However, applying current contrastive learning approaches to medical data without considering its domain-specific anatomical characteristics may lead to visual representations that are inconsistent in appearance and semantics. In this paper, we propose to improve visual representations of medical images via anatomy-aware contrastive learning (AWCL), which incorporates anatomy information to augment the positive/negative pa…
FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening
Hunan University（湖南大学）；The Hong Kong University of Science and Technology（香港科技大学）；Anhui University（安徽大学）；Shenzhen Maternity and Child Healthcare Hospital（深圳市妇幼保健院）；Sichuan Provincial Maternity and Child Health Care Hospital（四川省妇幼保健院）；National University of Singapore（新加坡国立大学）；Yunnan Maternal and Child Health Hospital（云南省妇幼保健院）；Zhejiang University（浙江大学）
MIT（麻省理工学院）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Fetal MRI is heavily constrained by unpredictable and substantial fetal motion that causes image artifacts and limits the set of viable diagnostic image contrasts. Current mitigation of motion artifacts is predominantly performed by fast, single-shot MRI and retrospective motion correction. Estimation of fetal pose in real time during MRI stands to benefit prospective methods to detect and mitigate fetal motion artifacts where inferred fetal motion is combined with online slice prescription with low-latency decision making. Current developments of deep reinforcement learning (DRL), offer a nov…
Shenzhen University（深圳大学）；Nanfang Medical University（南方医科大学）；The Chinese University of Hong Kong（香港中文大学）
Volumetric ultrasound has great potentials in promoting prenatal examinations. Automated solutions are highly desired to efficiently and effectively analyze the massive volumes. Segmentation and landmark localization are two key techniques in making the quantitative evaluation of prenatal ultrasound volumes available in clinic. However, both tasks are non-trivial when considering the poor image quality, boundary ambiguity and anatomical variations in volumetric ultrasound. In this paper, we propose an effective framework for simultaneous segmentation and landmark localization in prenatal ultra…
Isfahan University of Technology（伊斯法罕理工大学）；University of Michigan（密歇根大学）
Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of …
University College London（伦敦大学学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College London（伦敦大学学院伊丽莎白·加勒特·安德森妇女健康研究所）；NIHR University College London Hospitals Biomedical Research Centre（英国国家健康研究所伦敦大学学院医院生物医学研究中心）
During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a uni…
Sagol Brain Institute, Tel Aviv Sourasky Medical Center（特拉维夫苏拉斯基医疗中心萨戈尔脑研究所）；University College London（伦敦大学学院）
Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometry estimation depends on the precise detection of landmarks in standard ultrasound planes. Manual annotation can be time-consuming and operator dependent task, and may results in high measurements variability. Existing methods for automatic fetal biometry rely on initial automatic fetal structure segmentation followed by geometric landmark detection. However, segmentation annotations are time-consuming and may be inac…
USFetal: Tools for Fetal Brain Ultrasound Compounding
A. I. Virtanen Institute for Molecular Sciences, Faculty of Health Sciences, University of Eastern Finland（A.I.维塔内恩分子科学研究所，健康科学学院，东芬兰大学）；Munich Center for Machine Learning (MCML) at the Technical University of Munich（慕尼黑技术大学机器学习中心（MCML））；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Harvard Medical School and Boston Children’s Hospital（哈佛医学院和波士顿儿童医院）
Sano Centre for Computational Medicine（萨诺计算医学中心）；Informatics Institute, University of Amsterdam（阿姆斯特丹大学信息学研究所）；The Medical Centre of Postgraduate Education（研究生教育医学中心）；Poznan University of Medical Sciences（波兹南医科大学）；Medical University of Warsaw（华沙医科大学）；Warsaw University of Technology（华沙理工大学）
Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of delivery. Accurate prediction of weight using prenatal ultrasound is challenging as it requires images of specific fetal body parts during advanced pregnancy which is difficult to capture due to poor quality of images caused by the lack of amniotic fluid. As a consequence, predictions which rely on standard methods often suffer from significant errors. In this paper we propose the Residual Transformer Module which exte…
Google Health（谷歌健康）；University of Zambia School of Medicine（赞比亚大学医学院）；University of North Carolina School of Medicine（北卡罗来纳大学医学院）；UNC Global Projects – Zambia, LLC（北卡罗来纳大学全球项目赞比亚有限责任公司）
Despite considerable progress in maternal healthcare, maternal and perinatal deaths remain high in low-to-middle income countries. Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption. We developed and validated an artificial intelligence (AI) system that uses novice-acquired "blind sweep" ultrasound videos to estimate gestational age (GA) and fetal malpresentation. We further addressed obstacles that may be encountered in low-resourced settings. Using a simplified sweep protocol with real-time AI feedback …
Foundation Model-driven Key Anatomy Frame Selection for Blind-sweep Ultrasound Fetal Birth Weight Estimation
Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University（深圳大学医学超声图像计算实验室）；School of Biomedical Engineering, Shenzhen University（深圳大学生物医学工程学院）；School of Biomedical Engineering and Informatics, Nanjing Medical University（南京医科大学生物医学工程与信息学院）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University（青岛大学附属山东省妇幼保健院）；Jiangxi Maternal and Child Health Hospital（江西省妇幼保健院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）
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）
Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos. Approach. We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made …
Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程技术研究中心，武汉大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（computing and Data Science学院，南洋理工大学）
In this work, we present an empirical study of DCGANs, including hyperparameter heuristics and image quality assessment, as a way to address the scarcity of datasets to investigate fetal head ultrasound. We present experiments to show the impact of different image resolutions, epochs, dataset size input, and learning rates for quality image assessment on four metrics: mutual information (MI), Fréchet inception distance (FID), peak-signal-to-noise ratio (PSNR), and local binary pattern vector (LBPv). The results show that FID and LBPv have stronger relationship with clinical image quality score…
Aligning Fetal Anatomy with Kinematic Tree Log-Euclidean PolyRigid Transforms
Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA（麻省理工学院计算机科学与人工智能实验室）；California Institute of Technology, Pasadena, USA（加州理工学院）；Boston Children’s Hospital and Harvard Medical School, Boston, USA（哈佛医学院波士顿儿童医院）；The Hebrew University of Jerusalem, Jerusalem, Israel（耶路撒冷希伯来大学）
SonoCLIP: Mask-Guided Region-Aware Vision-Language Pretraining for Fetal Ultrasound Analysis
School of Computer Science, Wuhan University, Wuhan, China（武汉大学计算机学院，武汉，中国）；Institute of Artificial Intelligence, Wuhan University, Wuhan, China（武汉大学人工智能研究院，武汉，中国）；Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan, China（武汉大学仁民医院超声科，武汉，中国）；National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China（武汉大学多媒体软件国家工程研究中心，武汉，中国）；Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, China（湖北省多媒体与网络通信工程重点实验室，武汉大学，武汉，中国）
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Technical University of Denmark（丹麦技术大学）；Slagelse Hospital（斯莱格瑟医院）；CAMES；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院丽格医院）；University of Copenhagen（哥本哈根大学）
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（牛津大学工程科学系生物医学工程研究所）；Nuffield Department of Obstetrics and Gynaecology, John Radcliffe Hospital, University of Oxford（牛津大学约翰拉德克利夫医院妇产科）；Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford（牛津大学李兆基健康信息与发现中心大数据研究所）
To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, rega…
Utah State University（犹他州立大学）；Harbin Institute of Technology（哈尔滨工业大学）；University of Idaho（爱达荷大学）；The Affiliated Hospital of Qingdao University（青岛大学附属医院）；The Second Hospital of Hebei Medical University（河北医科大学第二医院）
Tumor saliency estimation aims to localize tumors by modeling the visual stimuli in medical images. However, it is a challenging task for breast ultrasound due to the complicated anatomic structure of the breast and poor image quality; and existing saliency estimation approaches only model generic visual stimuli, e.g., local and global contrast, location, and feature correlation, and achieve poor performance for tumor saliency estimation. In this paper, we propose a novel optimization model to estimate tumor saliency by utilizing breast anatomy. First, we model breast anatomy and decompose bre…
