Ultrasound imaging is generally employed for real-time investigation of internal anatomy of the human body for disease identification. Delineation of the anatomical boundary of organs and pathological lesions is quite challenging due to the stochastic nature of speckle intensity in the images, which also introduces visual fatigue for the observer. This paper introduces a fully convolutional neural network based method to segment organ and pathologies in ultrasound volume by learning the spatial-relationship between closely related classes in the presence of stochastically varying speckle intensity. We propose a convolutional encoder-decoder like framework with (i) feature concatenation across matched layers in encoder and decoder and (ii) index passing based unpooling at the decoder for semantic segmentation of ultrasound volumes. We have experimentally evaluated the performance on publicly available datasets consisting of $10$ intravascular ultrasound pullback acquired at $20$ MHz and $16$ freehand thyroid ultrasound volumes acquired $11 - 16$ MHz. We have obtained a dice score of $0.93 \pm 0.08$ and $0.92 \pm 0.06$ respectively, following a $10$-fold cross-validation experiment while processing frame of $256 \times 384$ pixel in $0.035$s and a volume of $256 \times 384 \times 384$ voxel in $13.44$s.
UC San Diego（加州大学圣地亚哥分校）；General Electric Global Research（通用电气全球研究中心）
Ultrasound scanning is essential in several medical diagnostic and therapeutic applications. It is used to visualize and analyze anatomical features and structures that influence treatment plans. However, it is both labor intensive, and its effectiveness is operator dependent. Real-time accurate and robust automatic detection and tracking of anatomical structures while scanning would significantly impact diagnostic and therapeutic procedures to be consistent and efficient. In this paper, we propose a deep learning framework to automatically detect and track a specific anatomical target structu…
Indian Institute of Technology（印度理工学院）；Purdue University（普渡大学）；All India Institute of Medical Sciences（全印度医学科学研究所）
We investigate the applicability of U-Net based models for segmenting Urinary Bladder (UB) in male pelvic view UltraSound (US) images. The segmentation of UB in the US image aids radiologists in diagnosing the UB. However, UB in US images has arbitrary shapes, indistinct boundaries and considerably large inter- and intra-subject variability, making segmentation a quite challenging task. Our study of the state-of-the-art (SOTA) segmentation network, U-Net, for the problem reveals that it often fails to capture the salient characteristics of UB due to the varying shape and scales of anatomy in t…
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 …
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）
Striving for Simplicity: Simple Yet Effective Prior-Aware Pseudo-Labeling for Semi-Supervised Ultrasound Image Segmentation
Wuhan University of Technology（武汉理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；MedAI Technology (Wuxi) Co. Ltd.（MedAI科技（无锡）有限公司）；Technical University of Munich（慕尼黑工业大学）
Robot-guided catheter insertion has the potential to deliver urgent medical care in situations where medical personnel are unavailable. However, this technique requires accurate and reliable segmentation of anatomical landmarks in the body. For the ultrasound imaging modality, obtaining large amounts of training data for a segmentation model is time-consuming and expensive. This paper introduces RESUS (RESlicing of UltraSound Images), a weak supervision data augmentation technique for ultrasound images based on slicing reconstructed 3D volumes from tracked 2D images. This technique allows us t…
Samara National Research University（萨马拉国立研究大学）；IPSI RAS（俄罗斯科学院图像投影系统研究所）；FSBEI HE SamSMU MOH Russia（俄罗斯卫生部萨马拉国立医科大学）；ICCS RAS（俄罗斯科学院复杂系统研究所）
In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation - U-Net. A comparison is made for the results of detection anatomical reference points for each of the selected neural network architectures and their comparison with the results obtained when orthodontists detected anatomical reference points. Based on the ob…
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…
The Chinese University of Hong Kong（香港中文大学）；Siemens Healthcare（西门子医疗）
Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has been widely used with superiorities such as low cost and portability, the fuzzy border definition and existence of abounding artifacts pose great challenges for automatically detecting and segmenting the complex anatomical structures. In this paper, we propose a multi-domain regularized deep learning method to address this challenging problem. By leveraging the transfer learning from cross domains, the feature represe…
School of Biomedical Engineering, Southern Medical University（南方医科大学生物医学工程学院）
Breast ultrasound (BUS) image segmentation plays a crucial role in a computer-aided diagnosis system, which is regarded as a useful tool to help increase the accuracy of breast cancer diagnosis. Recently, many deep learning methods have been developed for segmentation of BUS image and show some advantages compared with conventional region-, model-, and traditional learning-based methods. However, previous deep learning methods typically use skip-connection to concatenate the encoder and decoder, which might not make full fusion of coarse-to-fine features from encoder and decoder. Since the str…
Left ventricular segmentation is essential for measuring left ventricular function indices. Segmentation of one or several images requires an initial guess of the contour. It is hypothesized here that creating an initial guess by first detecting anatomical markers, would lead to correct detection of the endocardium. The first step of the algorithm presented here includes automatic detection of the mitral valve. Next, the apex is detected in the same frame. The valve is then tracked throughout the cardiac cycle. Contours passing from the apex to each valve corner are then found using a dynamic …
Interactive simulation of ultrasound imaging greatly facilitates sonography training. Although ray-tracing based methods have shown promising results, obtaining realistic images requires substantial modeling effort and manual parameter tuning. In addition, current techniques still result in a significant appearance gap between simulated images and real clinical scans. Herein we introduce a novel content-preserving image translation framework (ConPres) to bridge this appearance gap, while maintaining the simulated anatomical layout. We achieve this goal by leveraging both simulated images with …
The Chinese University of Hong Kong（香港中文大学）；University of Cambridge（剑桥大学）；Inception Institute of Artificial Intelligence（启迪人工智能研究院）；The Hong Kong University of Science and Technology（香港科技大学）；Jinan University（暨南大学）；Southern University of Science and Technology（南方科技大学）；Chinese Academy of Sciences（中国科学院）
Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which is one of the dreadful diseases that affect women globally. Segmenting breast regions accurately from ultrasound image is a challenging task due to the inherent speckle artifacts, blurry breast lesion boundaries, and inhomogeneous intensity distributions inside the breast lesion regions. Recently, convolutional neural networks (CNNs) have demonstrated remarkable results in medical image segmentation tasks. However, the convolutional operations in a CNN often focus on local regions, which suffer from limit…
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…
Ultrasound-CLIP: Semantic-Aware Contrastive Pre-training for Ultrasound Image-Text Understanding
Hangzhou City University（杭州城市大学）；Hong Kong Baptist University（香港浸会大学）；Zhejiang University（浙江大学）；The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院）；City University of Hong Kong（香港城市大学）
AnomExpert: Identifying and Selecting Anatomical Planes for Prenatal Ultrasound Anomaly Diagnosis
Medical Ultrasound Image Computing (MUSIC) Lab, School of Artificial Intelligence, Shenzhen University（医学超声图像计算（MUSIC）实验室，深圳大学人工智能学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件学院）；National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University（深圳大学大数据系统计算技术国家工程实验室）；School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；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）
Semantic Scene Graph for Ultrasound Image Explanation and Scanning Guidance
Computer Aided Medical Procedures (CAMP)（计算机辅助医疗程序）；TU Munich, Germany（慕尼黑工业大学，德国）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；The Chinese University of Hong Kong, Hong Kong, China（香港中文大学，中国香港）
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…
Ultrasound is the second most used modality in medical imaging. It is cost effective, hazardless, portable and implemented routinely in numerous clinical procedures. Nonetheless, image quality is characterized by granulated appearance, poor SNR and speckle noise. Specific for malignant tumors, the margins are blurred and indistinct. Thus, there is a great need for improving ultrasound image quality. We hypothesize that this can be achieved, using neural networks, by translation into a more realistic display which mimics an anatomical cut through the tissue. In order to achieve this goal, the p…
Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e.g., due to breathing. One way to formulate this tracking problem is the automatic localization of given tracked anatomical landmarks throughout a temporal ultrasound sequence. For this, we herein propose a fully-convolutional Siamese network that learns the similarity between pairs of image regions containing the same landmark. Accordingly, it learns to localize and thus track arbitrary image features, not only predefine…
T2ID-CAS: Diffusion Model and Class Aware Sampling to Mitigate Class Imbalance in Neck Ultrasound Anatomical Landmark Detection
Department of Computer Science, University of Central Florida（计算机科学系，中央佛罗里达大学）；Department of Otolaryngology Head Neck Surgery, University of Cincinnati College of Medicine（耳鼻喉科与头颈外科系，辛辛那提大学医学院）；Department of Internal Medicine, University of Central Florida College of Medicine（内科系，中央佛罗里达大学医学院）
Anatomical Prior-Driven Framework for Autonomous Robotic Cardiac Ultrasound Standard View Acquisition
State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学智能制造装备与技术国家重点实验室）；School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）生物医学工程学院）；Information Intelligence Lab, Department of Electrical Engineering, National Chung Hsing University（中原大学电子工程系信息智能实验室）；Institute of Medical Equipment Science and Engineering, Huazhong University of Science and Technology（华中科技大学医学装备科学与工程研究院）；Department of Ultrasound Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属同济医院超声医学科）；Institute of Systems Science (ISS), National University of Singapore (NUS)（新加坡国立大学系统科学研究所）
Medical imaging based prostate cancer diagnosis procedure uses intra-operative transrectal ultrasound (TRUS) imaging to visualize the prostate shape and location to collect tissue samples. Correct tissue sampling from prostate requires accurate prostate segmentation in TRUS images. To achieve this, this study uses a novel residual connection based fully convolutional network. The advantage of this segmentation technique is that it requires no pre-processing of TRUS images to perform the segmentation. Thus, it offers a faster and straightforward prostate segmentation from TRUS images. Results s…
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 …
Shenzhen University（深圳大学）；University of Leeds（利兹大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳睿影医疗科技有限公司）
Standard plane (SP) localization is essential in routine clinical ultrasound (US) diagnosis. Compared to 2D US, 3D US can acquire multiple view planes in one scan and provide complete anatomy with the addition of coronal plane. However, manually navigating SPs in 3D US is laborious and biased due to the orientation variability and huge search space. In this study, we introduce a novel reinforcement learning (RL) framework for automatic SP localization in 3D US. Our contribution is three-fold. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the…
Multivariate Gaussian NeRF for Wide Field-of-View Ultrasound Reconstruction
Chair for Computer Aided Medical Procedures (CAMP), Technical University of Munich, Germany（计算机辅助医学程序系（CAMP），慕尼黑技术大学，德国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）；LUMA Vision, Beech Hill Road, Dublin, Ireland（LUMA视觉，Beech Hill Road，都柏林，爱尔兰）
We consider the problem of segmenting a biomedical image into anatomical regions of interest. We specifically address the frequent scenario where we have no paired training data that contains images and their manual segmentations. Instead, we employ unpaired segmentation images to build an anatomical prior. Critically these segmentations can be derived from imaging data from a different dataset and imaging modality than the current task. We introduce a generative probabilistic model that employs the learned prior through a convolutional neural network to compute segmentations in an unsupervise…
National University of Defense Technology（国防科技大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Tongji University（同济大学）
Ultrasound tongue imaging is widely used for speech production research, and it has attracted increasing attention as its potential applications seem to be evident in many different fields, such as the visual biofeedback tool for second language acquisition and silent speech interface. Unlike previous studies, here we explore the feasibility of age estimation using the ultrasound tongue image of the speakers. Motivated by the success of deep learning, this paper leverages deep learning on this task. We train a deep convolutional neural network model on the UltraSuite dataset. The deep model ac…
Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校放射学系与生物医学研究中心）；Lampe Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校兰普生物医学工程联合系）
US-X Complete: A Multi-Modal Approach to Anatomical 3D Shape Recovery
Computer-Aided Medical Procedures (CAMP), Technical University of Munich, Munich, Germany（计算机辅助医学程序（CAMP），慕尼黑技术大学，慕尼黑，德国）；Munich Center for Machine Learning (MCML), Germany（慕尼黑机器学习中心（MCML），德国）；Konrad Zuse School of Excellence in Reliable AI (relAI), Germany（康拉德·祖斯卓越可靠人工智能学校（relAI），德国）
This paper concerns the fully automatic direct in vivo measurement of active and passive dynamic skeletal muscle states using ultrasound imaging. Despite the long standing medical need (myopathies, neuropathies, pain, injury, ageing), currently technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal intramuscular states. Ultrasound provides a technology in which static and dynamic muscle states can be observed non-invasively, yet current computational image understanding approaches are inadequate. We propose a ne…
SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound
School of Computer Science, Wuhan University（武汉大学计算机学院）；Hubei Center for Applied Mathematics, Wuhan University（湖北应用数学中心，武汉大学）；Department of Ultrasound, The Central Hospital of Wuhan（武汉市中心医院超声科）；Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology（同济医院，同济医学院，华中科技大学医学影像科）；Department of Ultrasound in Medicine, Renmin Hospital of Wuhan University（武汉大学仁医医院医学超声科）；University Hospital, Johann-Wolfgang-Goethe University Frankfurt am Main（法兰克福歌德大学医学院大学医院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
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））
Prior-guided Hierarchical Instance-pixel Contrastive Learning for Ultrasound Speckle Noise Suppression
School of Biological Science and Medical Engineering, Southeast University（生物科学与医学工程学院，东南大学）；Department of Biomedical Engineering, The Ohio State University（生物医学工程系，奥斯特大学）；Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University（江苏省生物材料与器件重点实验室，东南大学）；State Key Laboratory of Digital Medical Engineering, Southeast University（国家数字医学工程重点实验室，东南大学）
Shenzhen University（深圳大学）；University of Leeds（利兹大学）；KU Leuven（鲁汶大学）；Luohu People’s Hospital（罗湖人民医院）
Standard plane recognition plays an important role in prenatal ultrasound (US) screening. Automatically recognizing the standard plane along with the corresponding anatomical structures in US image can not only facilitate US image interpretation but also improve diagnostic efficiency. In this study, we build a novel multi-label learning (MLL) scheme to identify multiple standard planes and corresponding anatomical structures of fetus simultaneously. Our contribution is three-fold. First, we represent the class correlation by word embeddings to capture the fine-grained semantic and latent stati…
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）
UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound（医学超声关键技术研发实验室）；School of Biomedical Engineering, Medical School, Shenzhen University（生物医学工程学院）；Faculty of Applied Sciences, Macao Polytechnic University（应用科学学院）；Centre for CIMIM, Manchester University（CIMIM研究中心）；Center for Cardiovascular Disease（心血管疾病中心）；The Affiliated Suzhou Hospital of Nanjing Medical University（南京医科大学附属苏州医院）；Suzhou Municipal Hospital, Gusu School, Suzhou, China（苏州 municipal 医院）；The First Affiliated Hospital of Kunming Medical University（昆明医科大学第一附属医院）；The First Affiliated Hospital of Anhui Medical University North（安徽医科大学北院）；The Second People’s Hospital（第二人民医院）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；The People's Hospital of Tongren City（铜仁市人民医院）；The First People's Hospital of Taicang City（太仓市第一人民医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
OpenUS: A Fully Open-Source Foundation Model for Ultrasound Image Analysis via Self-Adaptive Masked Contrastive Learning
Digital Environment Research Institute (DERI), Queen Mary University of London（女王玛丽大学数字环境研究 institute（DERI））；The William Harvey Research Institute, Queen Mary University of London（女王玛丽大学威廉·哈维研究 institute）；School of Electronic Engineering and Computer Science, Queen Mary University of London（女王玛丽大学电子工程与计算机科学学院）
Institute for Robotics and Cognitive Systems, Universität zu Lübeck（吕贝克大学机器人与认知系统研究所）
3D ultrasound (3DUS) becomes more interesting for target tracking in radiation therapy due to its capability to provide volumetric images in real-time without using ionizing radiation. It is potentially usable for tracking without using fiducials. For this, a method for learning meaningful representations would be useful to recognize anatomical structures in different time frames in representation space (r-space). In this study, 3DUS patches are reduced into a 128-dimensional r-space using conventional autoencoder, variational autoencoder and sliced-wasserstein autoencoder. In the r-space, the…
