Investigation and Assessment of Disorder of Ultrasound B-mode Images
Samrat Ashok Technological Institute（萨姆拉特·阿肖克技术学院）；Department of Trg. & Technical Education, Govt. of NCT Delhi（德里国家首都辖区政府培训与技术教育局）
Vidhi Rawat, Alok Jain, Vibhakar Shrimali
备注：
Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis/
Digital image plays a vital role in the early detection of cancers, such as prostate cancer, breast cancer, lungs cancer, cervical cancer. Ultrasound imaging method is also suitable for early detection of the abnormality of fetus. The accurate detection of region of interest in ultrasound image is crucial. Since the result of reflection, refraction and deflection of ultrasound waves from different types of tissues with different acoustic impedance. Usually, the contrast in ultrasound image is very low and weak edges make the image difficult to identify the fetus region in the ultrasound image. So the analysis of ultrasound image is more challenging one. We try to develop a new algorithmic approach to solve the problem of non clarity and find disorder of it. Generally there is no common enhancement approach for noise reduction. This paper proposes different filtering techniques based on statistical methods for the removal of various noise. The quality of the enhanced images is measured by the statistical quantity measures: Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Root Mean Square Error (RMSE).
Pierre and Marie Curie University（皮埃尔和玛丽居里大学）；ESPCI Paris, PSL Research University（巴黎高等物理化工学院，巴黎文理研究大学）；National University of Defense Technology（国防科技大学）；Peking University（北京大学）；Sun Yat-Sen University（中山大学）
During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically. Yet, despite of the remarkable progress that has been made, the medical ultrasound image similarity measurement remains largely unsolved due to a high level of speckle noise contamination. Potential applications of the ultrasound image similarity measurement seem evident in several aspects. To name a few, ultrasound imaging quality assessment, abnormal function region detection, etc. In this paper, a comparative study was made on full-reference image quality assessment…
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）
A Causality-Inspired Model for Intima-Media Thickening Assessment in Ultrasound Videos
School of Biological Science and Medical Engineering, Southeast University（东南大学生物科学与医学工程学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences（中国医学科学院北京协和医学院医院）
Automated Quality Assessment of Blind Sweep Obstetric Ultrasound for Improved Diagnosis
Nepal Applied Mathematics and Informatics Institute for research (NAAMII)（尼泊尔应用数学与信息学研究所（NAAMII））；GE HealthCare（GE医疗）；Institute of Medicine (IOM), Tribhuvan University（特里布万大学医学院（IOM））
Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering
All India Institute of Medical Sciences (AIIMS)（全印医学科学学院）；Purdue University (PU)（普渡大学）；Indian Institute of Technology (IIT)（印度理工学院）；Berea College（伯里亚学院）
Graz University of Technology（格拉茨工业大学）；Ulm University（乌尔姆大学）；Shanghai Jiao Tong University（上海交通大学）；BioTechMed-Graz（格拉茨生物科技医疗）；Katharinenhospital（卡塔琳娜医院）
The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in US images of the liver with two specialists in Internal Medicine. Thereby, evaluating the approach with over one hundred different acquisitions of metastases. The two physicians or the algor…
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）
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…
The Hong Kong Polytechnic University（香港理工大学）；Research Institute for Smart Ageing, PolyU（香港理工大学智衰研究所）
The scoliosis progression in adolescents requires close monitoring to timely take treatment measures. Ultrasound imaging is a radiation-free and low-cost alternative in scoliosis assessment to X-rays, which are typically used in clinical practice. However, ultrasound images are prone to speckle noises, making it challenging for sonographers to detect bony features and follow the spine's curvature. This paper introduces a robotic-ultrasound approach for spinal curvature tracking and automatic navigation. A fully connected network with deconvolutional heads is developed to locate the spinous pro…
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）
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…
AlignUS: MRI-Guided Ultrasound Representation Learning for ALS Classification from Tongue Images
Unité de Recherche en Systèmes Intelligents Avancés (URSIA), Institut Supérieur du Numérique (SupNum)（高级智能系统研究单位（URSIA），数字高等学院（SupNum））；University of Basel（巴塞尔大学）
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory. However, GA estimation based on CRL, requires proper positioning of calipers on the fetal crown and rump view, which is not always an easy plane to find, especially for an inexperienced sonographer. Finding a slightly oblique view from the true CRL view could lead to a different CRL value and therefore incorrect estimation of GA. This study presents an AI-based method for a quality assessment of the CR…
PAII Inc.（PAII公司）；Chang Gung Memorial Hospital（长庚纪念医院）；Vanderbilt University（范德堡大学）；PingAn Technology（平安科技）
Ultrasound (US) is a critical modality for diagnosing liver fibrosis. Unfortunately, assessment is very subjective, motivating automated approaches. We introduce a principled deep convolutional neural network (CNN) workflow that incorporates several innovations. First, to avoid overfitting on non-relevant image features, we force the network to focus on a clinical region of interest (ROI), encompassing the liver parenchyma and upper border. Second, we introduce global heteroimage fusion (GHIF), which allows the CNN to fuse features from any arbitrary number of images in a study, increasing its…
Université Pierre et Marie Curie（皮埃尔和玛丽·居里大学）；Langevin Institute, ESPCI-ParisTech（巴黎高等物理化工学院朗之万研究所）；University of New Mexico（新墨西哥大学）；Tianjin University（天津大学）
This article describes a contour-based 3D tongue deformation visualization framework using B-mode ultrasound image sequences. A robust, automatic tracking algorithm characterizes tongue motion via a contour, which is then used to drive a generic 3D Finite Element Model (FEM). A novel contour-based 3D dynamic modeling method is presented. Modal reduction and modal warping techniques are applied to model the deformation of the tongue physically and efficiently. This work can be helpful in a variety of fields, such as speech production, silent speech recognition, articulation training, speech dis…
Medical University of Warsaw（华沙医科大学）；University of Warsaw（华沙大学）；SGH Warsaw School of Economics（SGH华沙经济学院）；Carolina Medical Center（卡罗莱纳医疗中心）；Warsaw University of Technology（华沙理工大学）；Tooploox
Achilles tendon rupture is a debilitating injury, which is typically treated with surgical repair and long-term rehabilitation. The recovery, however, is protracted and often incomplete. Diagnosis, as well as healing progress assessment, are largely based on ultrasound and magnetic resonance imaging. In this paper, we propose an automatic method based on deep learning for analysis of Achilles tendon condition and estimation of its healing progress on ultrasound images. We develop custom convolutional neural networks for classification and regression on healing score and feature extraction. Our…
ShanghaiTech University（上海科技大学）；Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海高等研究院）；University of Chinese Academy of Sciences（中国科学院大学）；The Chinese University of Hong Kong（香港中文大学）
Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but current assessment method only uses coronal projection image and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch, a two-step framework to detect vertebral structures in 3D ultrasound volume by utilizing unlabeled data in semi-supervised manner. The first step is to detect the possible positions of structures on transv…
A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound
Medical Informatics, University of Lübeck（吕贝克大学医学信息学系）；Institut of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院放射学与核医学研究所）；Paediatric Surgery, University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院小儿外科）；EchoScout GmbH
College of Science, Liaoning Petrochemical University（辽宁石油化工大学理学院）；Industrial Mathematics Laboratory, Baikal School of BRICS, Irkutsk National Research Technical University（伊尔库茨克国立研究技术大学贝加尔金砖学院工业数学实验室）
Biodegradable elastic scaffolds have attracted more and more attention in the field of soft tissue repair and tissue engineering. These scaffolds made of porous bioelastomers support tissue ingrowth along with their own degradation. It is necessary to develop a computer-aided analyzing method based on ultrasound images to identify the degradation performance of the scaffold, not only to obviate the need to do destructive testing, but also to monitor the scaffold's degradation and tissue ingrowth over time. It is difficult using a single traditional image processing algorithm to extract continu…
King’s College London（伦敦国王学院）；IMT Atlantique（大西洋国立高等矿业电信学校）；Johns Hopkins University（约翰斯·霍普金斯大学）；Oxford University（牛津大学）；University Medical Center Utrecht（乌得勒支大学医学中心）；King’s College Hospital（国王学院医院）；University of Gondar（贡德尔大学）
In many low-to-middle income (LMIC) countries, ultrasound is used for assessment of pleural effusion. Typically, the extent of the effusion is manually measured by a sonographer, leading to significant intra-/inter-observer variability. In this work, we investigate the use of deep learning (DL) to automate the process of pleural effusion segmentation from ultrasound images. On two datasets acquired in a LMIC setting, we achieve median Dice Similarity Coefficients (DSCs) of 0.82 and 0.74 respectively using the nnU-net DL model. We also investigate the use of coordinate convolutions in the DL mo…
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）
University of Leicester（莱斯特大学）；University Hospitals of Leicester NHS Trust（莱斯特大学医院NHS信托）；Affiliated Hospital of Nantong University（南通大学附属医院）
Accurate tracking of an anatomical landmark over time has been of high interests for disease assessment such as minimally invasive surgery and tumor radiation therapy. Ultrasound imaging is a promising modality benefiting from low-cost and real-time acquisition. However, generating a precise landmark tracklet is very challenging, as attempts can be easily distorted by different interference such as landmark deformation, visual ambiguity and partial observation. In this paper, we propose a long-short diffeomorphic motion network, which is a multi-task framework with a learnable deformation prio…
FreqDINO++: A Frequency-Guided Multi-Task Routing Vision Foundation Model for Universal Ultrasound Analysis
University of Nottingham Ningbo China（宁波诺丁汉大学）；University of Nottingham（诺丁汉大学）；University of Lincoln（林肯大学）；Jinan University（暨南大学）；The Hong Kong Polytechnic University（香港理工大学）
UltraUNet: Real-Time Ultrasound Tongue Segmentation for Diverse Linguistic and Imaging Conditions
Biomedical Engineering Department（生物医学工程系）；Hong Kong Polytechnic University（香港理工大学）；English and Communication Department（英语与交流系）；Department of Chinese and Bilingual Studies（中文与双语研究系）；Department of Biomedical Engineering Research Institute for Smart Ageing（生物医学工程研究 institute for Smart Ageing）
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）
Sequential Spatial-Temporal Network for Interpretable Automatic Ultrasonic Assessment of Fetal Head during labor
School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；Sydney School of Health Sciences, The University of Sydney（悉尼大学悉尼健康科学学院）；School of Science, Edith Cowan University（伊迪丝·考恩大学理学院）；Orange Hospital（奥兰治医院）
Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer
Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images
Oxford Machine Learning in NeuroImaging Lab, Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Visual Geometry Group, Department of Engineering Science, University of Oxford（牛津大学工程科学系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算科学与数据科学学院）
Concordia University（康考迪亚大学）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Western University（韦仕敦大学）；St. Olavs Hospital（圣奥拉夫医院）；Trondheim University Hospital（特隆赫姆大学医院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）；SINTEF Digital（SINTEF数字研究所）
Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images play an essential role in surgical planning and resection of brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique annotation dataset of tumor tissues and resection cavities from the previously published RESECT dataset (Xiao et al. 2017) to encourage a more rigorous assessments of image processing techniques. Acquisition and validation methods: The RESECT database …
ShanghaiTech University（上海科技大学）；University of Alberta（阿尔伯塔大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
Ultrasound spine imaging technique has been applied to the assessment of spine deformity. However, manual measurements of scoliotic angles on ultrasound images are time-consuming and heavily rely on raters experience. The objectives of this study are to construct a fully automatic framework based on Faster R-CNN for detecting vertebral lamina and to measure the fitting spinal curves from the detected lamina pairs. The framework consisted of two closely linked modules: 1) the lamina detector for identifying and locating each lamina pairs on ultrasound coronal images, and 2) the spinal curvature…
In healthcare, it is essential to explain the decision-making process of machine learning models to establish the trustworthiness of clinicians. This paper introduces BI-RADS-Net, a novel explainable deep learning approach for cancer detection in breast ultrasound images. The proposed approach incorporates tasks for explaining and classifying breast tumors, by learning feature representations relevant to clinical diagnosis. Explanations of the predictions (benign or malignant) are provided in terms of morphological features that are used by clinicians for diagnosis and reporting in medical pra…
University of Waterloo（滑铁卢大学）；McGill University（麦吉尔大学）；St. Mary’s Hospital（圣玛丽医院）；Waterloo AI Institute（滑铁卢人工智能研究院）；DarwinAI Corp.（达尔文人工智能公司）
As the global population continues to face significant negative impact by the on-going COVID-19 pandemic, there has been an increasing usage of point-of-care ultrasound (POCUS) imaging as a low-cost and effective imaging modality of choice in the COVID-19 clinical workflow. A major barrier with widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians that can interpret POCUS examinations, leading to considerable interest in deep learning-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networ…
Fetal ultrasounds are an essential part of prenatal care and can be used to estimate gestational age (GA). Accurate GA assessment is important for providing appropriate prenatal care throughout pregnancy and identifying complications such as fetal growth disorders. Since derivation of GA from manual fetal biometry measurements (head, abdomen, femur) are operator-dependent and time-consuming, there have been a number of research efforts focused on using artificial intelligence (AI) models to estimate GA using standard biometry images, but there is still room to improve the accuracy and reliabil…
Do We Need Pre-Processing for Deep Learning Based Ultrasound Shear Wave Elastography?
Hamburg University of Technology（汉堡工业大学）；Institute of Medical Technology and Intelligent Systems（医学技术与智能系统研究所）；SustAInLivWork Center of Excellence（SustAInLivWork卓越中心）
Towards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging
Centre for Mathematical Sciences, Lund University（隆德大学数学中心）；Department of Diagnostic Radiology, Translational Medicine, Lund University（隆德大学转化医学系诊断放射学系）；Unilabs Mammography Unit, Skåne University Hospital（斯科讷大学医院Unilabs乳腺钼靶检查科）
Rensselaer Polytechnic Institute（伦斯勒理工学院）；University at Buffalo-State University of New York（纽约州立大学布法罗分校）；U.S. Army Futures Command（美国陆军未来司令部）；Combat Capabilities Development Command Soldier Center（作战能力发展司令部士兵中心）
Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents a deep convolutional neural network to classify burn depth based on altered tissue morphology of burned skin manifested as texture patterns in the ultrasound images. The network first learns a low-dimensional manifold of the unburned skin images using an encoder-decoder architecture that reconstructs it from ultrasound images of burned skin. The encoder is then re-trained to classify burn depths. The encoder-decoder network is trained using a dataset comprised of B-mode ultrasound images of unbu…
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…
University College London（伦敦大学学院）；InstaDeep（InstaDeep公司）；Stanford School of Medicine（斯坦福医学院）
In this paper, we consider a type of image quality assessment as a task-specific measurement, which can be used to select images that are more amenable to a given target task, such as image classification or segmentation. We propose to train simultaneously two neural networks for image selection and a target task using reinforcement learning. A controller network learns an image selection policy by maximising an accumulated reward based on the target task performance on the controller-selected validation set, whilst the target task predictor is optimised using the training set. The trained con…
FAMSeg: Fetal Femur and Cranial Ultrasound Segmentation Using Feature-Aware Attention and Mamba Enhancement
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）；Macau University of Science and Technology（澳门科技大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Shenzhen Maternity and Child Healthcare Hospital（深圳妇幼保健医院）；Southern Medical University（南方医科大学）；College of Big Data and Software Engineering（大数据与软件工程学院）；College of Electronical and Information Engineering（电子与信息工程学院）
