Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite a large number of survey papers already present in this field, most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 145 research papers published after 2017. Each paper is analyzed and commented on from both the methodology and application perspective. We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis, and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice.
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）
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…
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for anomaly screening. For this ultrasound (US) is employed. While expert sonographers are adept at reading US images, MR images are much easier for non-experts to interpret. Hence in this paper we seek to produce images with MRI-like appearance directly from clinical US images. Our own clinical motivation is to seek a way to communicate US findings to patients or clinical professionals unfamiliar with US, but in medical image analysis such a capability is potentially useful, for in…
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）
Multi-Center Study on Deep Learning-Assisted Detection and Classification of Fetal Central Nervous System Anomalies Using Ultrasound Imaging
School of Mathematics and Statistics, Xiamen University of Technology（厦门理工学院数学与统计学院）；Department of Obstetrics, Xiamen University Affiliated First Hospital（厦门大学附属第一医院妇产科）
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…
Imperial College London（伦敦帝国理工学院）；Technical University Munich（慕尼黑工业大学）
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an interesting and difficult challenge. This problem occurs frequently in medical imaging applications when attempts are made to deploy and improve deep learning models across different image acquisition devices, across acquisition parameters or if some classes are unavailable in new training databases. To address this problem, we propose Mutual Information…
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）
Universitat de Barcelona（巴塞罗那大学）；HP Inc.（惠普公司）；Suez University（苏伊士大学）；Nile University（尼罗河大学）；Intixel（Intixel公司）；University of Ghana（加纳大学）；University of London（伦敦大学）；Kamuzu University of Health Sciences（卡穆祖健康科学大学）；University of M’Hamed Bougara Boumerdes（穆罕默德·布格拉·布迈德斯大学）；Algiers University（阿尔及尔大学）；Mulago National Referral and Teaching Hospital（穆拉戈国家转诊教学医院）；Makerere University College of Health Sciences（马克雷雷大学健康科学学院）
Most artificial intelligence (AI) research have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify stand…
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）
HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation
University of Information Technology（信息科技大学）；Ho Chi Minh University of Technology（胡志明市技术大学）；University of Science（科学大学）；Vietnam National University（越南国家大学）；Boston University（波士顿大学）；Taipei Medical University（台北医学大学）；Posts and Telecommunications Institute of Technology（邮电技术学院）；New Mexico State University（新墨西哥州立大学）
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…
King’s College London（伦敦国王学院）；Imperial College London（帝国理工学院）
Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is useful for retrospective analysis including population based studies. However, such volume reconstructions require information about relative transformations between probe positions from which the individual volumes were acquired. In prenatal US scans, the fetus can move indepen…
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 …
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…
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…
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…
Deep learning models with large learning capacities often overfit to medical imaging datasets. This is because training sets are often relatively small due to the significant time and financial costs incurred in medical data acquisition and labelling. Data augmentation is therefore often used to expand the availability of training data and to increase generalization. However, augmentation strategies are often chosen on an ad-hoc basis without justification. In this paper, we present an augmentation policy search method with the goal of improving model classification performance. We include in …
3DFETUS: Deep Learning-Based Standardization of Facial Planes in 3D Ultrasound
Department of Engineering, Universitat Pompeu Fabra（工程系，庞培法布拉大学）；Department of Obstetrics and Gynecology, Hospital del Mar（妇产科，马尔医院）；Department of Robotics, Universitat Politecnica de Catalunya（机器人学系，加泰罗尼亚理工大学）；Gynecology and Reproductive Medicine, University Hospital Quirón Dexeus（妇产科与生殖医学，Quirón Dexeus大学医院）
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））
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 …
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；City, University of London（伦敦城市大学）；Technical University of Munich（慕尼黑工业大学）
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present an interpretable, atlas-learning segmentation method for automatic diagnosis of Hypo-plastic Left Heart Syndrome (HLHS) from a single `4 Chamber Heart' view image. We propose to extend the recently introduced Image-and-Spatial Transformer Networks (Atlas-ISTN) into a framework t…
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 …
Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn't received a lot of attention in the medical image analysis community. In the clinical practice, it is challenging to utilise raw diagnostic video data efficiently as video data takes a long time to process, annotate or audit. In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video data. Our approach is framed as reinforcement learning problem and produces agents focusing on the prese…
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…
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.
Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
Harvard Medical School（哈佛医学院）；Shenzhen University（深圳大学）；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）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；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）
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…
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）
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 …
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…
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…
Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition
University of Oxford（牛津大学）；University of Amsterdam（阿姆斯特丹大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Imperial College London（伦敦帝国理工学院）；University of Copenhagen（哥本哈根大学）
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）
University of Oxford（牛津大学）；Amazon（亚马逊）；Kings College London（伦敦国王学院）
This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries. Specifically, we leverage high-end (HE) ultrasound images to build a biometry solution for low-cost (LC) point-of-care ultrasound images. We propose a novel unsupervised domain adaptation approach to train deep models to be invariant to significant image distribution shift between the image types. Our proposed method, which employs a Dual Adversarial Calibration (DAC) framework, consists of adversarial pathways which enforce model invariance to; i) adversarial perturb…
One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms. In this paper we propose a novel representation disentanglement method to extract semantically meaningful and generalizable features for different tasks within a multi-task learning framework. Deep neural networks are utilized to ensure that the encoded features are…
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…
Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review
Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg（卢森堡大学生命科学与医学系，科学、技术与医学学院）；Department of Biostatistics and Medical Informatics, Institute of Health Sciences, Ege University（伊兹密尔大学健康科学学院生物统计学与医学信息学系）
