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）
Deepak Raina, Dimitrios Ntentia, SH Chandrashekhara, Richard Voyles, Subir Kumar Saha
备注：
Accepted in IEEE International Conference on Robotics and Automation (ICRA) 2023
Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic procedures. However, the diagnosis by ultrasound relies heavily on the quality of images assessed manually by sonographers, which diminishes the objectivity of the diagnosis and makes it operator-dependent. The supervised learning-based methods for automated quality assessment require manually annotated datasets, which are highly labour-intensive to acquire. These ultrasound images are low in quality and suffer from noisy annotations caused by inter-observer perceptual variations, which hampers learning efficiency. We propose an UnSupervised UltraSound image Quality assessment Network, US2QNet, that eliminates the burden and uncertainty of manual annotations. US2QNet uses the variational autoencoder embedded with the three modules, pre-processing, clustering and post-processing, to jointly enhance, extract, cluster and visualize the quality feature representation of ultrasound images. The pre-processing module uses filtering of images to point the network's attention towards salient quality features, rather than getting distracted by noise. Post-processing is proposed for visualizing the clusters of feature representations in 2D space. We validated the proposed framework for quality assessment of the urinary bladder ultrasound images. The proposed framework achieved 78% accuracy and superior performance to state-of-the-art clustering methods.
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…
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…
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…
Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation
University College London（伦敦大学学院）；City University of Hong Kong（香港城市大学）；InstaDeep（InstaDeep公司）；University College London Hospital NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）
King’s College London（伦敦国王学院）；Technical University of Munich（慕尼黑工业大学）；City, University of London（伦敦城市大学）；Imperial College London（帝国理工学院）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the mo…
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…
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…
University of Notre Dame（圣母大学）；University of Pittsburgh（匹兹堡大学）；George Mason University（乔治梅森大学）；Indiana University School of Medicine（印第安纳大学医学院）
Point-of-care ultrasound (POCUS) is one of the most commonly applied tools for cardiac function imaging in the clinical routine of the emergency department and pediatric intensive care unit. The prior studies demonstrate that AI-assisted software can guide nurses or novices without prior sonography experience to acquire POCUS by recognizing the interest region, assessing image quality, and providing instructions. However, these AI algorithms cannot simply replace the role of skilled sonographers in acquiring diagnostic-quality POCUS. Unlike chest X-ray, CT, and MRI, which have standardized ima…
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…
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…
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…
University College London（伦敦大学学院）；Frimley Park Hospital, Frimley Health NHS Foundation Trust（弗林利公园医院，弗林利健康NHS基金会信托）；Royal Berkshire Hospital, Royal Berkshire NHS Foundation Trust（皇家伯克郡医院，皇家伯克郡NHS基金会信托）；UCL Queen Square Institute of Neurology（伦敦大学学院皇后广场神经病学研究所）；University of Pavia（帕维亚大学）
We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models: a quality assessment module that automates predictions of image quality, and a diagnosis assistance module that determines the likelihood-oh-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for train…
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…
Indian Institute of Technology Palakkad（帕拉卡德印度理工学院）；University of Alberta（阿尔伯塔大学）
Ultrasound examination for detecting fractures is ideally suited for Emergency Departments (ED) as it is relatively fast, safe (from ionizing radiation), has dynamic imaging capability and is easily portable. High interobserver variability in manual assessment of ultrasound scans has piqued research interest in automatic assessment techniques using Deep Learning (DL). Most DL techniques are supervised and are trained on large numbers of labeled data which is expensive and requires many hours of careful annotation by experts. In this paper, we propose an unsupervised, domain specific transporte…
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）
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…
Influence of High-Performance Image-to-Image Translation Networks on Clinical Visual Assessment and Outcome Prediction: Utilizing Ultrasound to MRI Translation in Prostate Cancer
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）
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）
The assessment of the blood volume is crucial for the management of many acute and chronic diseases. Recent studies have shown that circulating blood volume correlates with the cross-sectional area (CSA) of the internal jugular vein (IJV) estimated from ultrasound imagery. In this paper, a semi-automatic segmentation algorithm is proposed using a combination of region growing and active contour techniques to provide a fast and accurate segmentation of IJV ultrasound videos. The algorithm is applied to track and segment the IJV across a range of image qualities, shapes, and temporal variation. …
Philips Research North America（飞利浦北美研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Accurate, real-time segmentation of vessel structures in ultrasound image sequences can aid in the measurement of lumen diameters and assessment of vascular diseases. This, however, remains a challenging task, particularly for extremely small vessels that are difficult to visualize. We propose to leverage the rich spatiotemporal context available in ultrasound to improve segmentation of small-scale lower-extremity arterial vasculature. We describe efficient deep learning methods that incorporate temporal, spatial, and feature-aware contextual embeddings at multiple resolution scales while join…
Automatic Ultrasound Curve Angle Measurement via Affinity Clustering for Adolescent Idiopathic Scoliosis Evaluation
The Hong Kong Polytechnic University（香港理工大学）；The Chinese University of Hong Kong（香港中文大学）；Joint Scoliosis Research Center of the Chinese University of Hong Kong and Nanjing University（香港中文大学与南京大学联合脊柱侧弯研究中心）；Nanjing University（南京大学）
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…
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…
School of Electrical Engineering and Computer Science, University of Ottawa（渥太华大学电气工程与计算机科学学院）
The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation. Owing to the development of computational units, availability of digital datasets, and improved performance of deep learning models, fully automatic and accurate tracking of tongue contours in real-time ultrasound data became practical only in recent years. Recent studies have shown that the performance of deep learning techniques is significant in the tracking of ultrasound tongue contours in real-time applications such as…
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）
Government Medical College, Thiruvananthapuram（特里凡得琅政府医学院）；Hospital Universitario Puerta de Hierro（普埃尔塔德耶罗大学医院）
Ultrasound is fast becoming an inevitable diagnostic tool for regular and continuous monitoring of the lung with the recent outbreak of COVID-19. In this work, a novel approach is presented to extract acoustic propagation-based features to automatically highlight the region below pleura, which is an important landmark in lung ultrasound (LUS). Subsequently, a multichannel input formed by using the acoustic physics-based feature maps is fused to train a neural network, referred to as LUSNet, to classify the LUS images into five classes of varying severity of lung infection to track the progress…
Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer
We recently published a deep learning study on the potential of encoder-decoder networks for the segmentation of the 2D CAMUS ultrasound dataset. We propose in this abstract an extension of the evaluation criteria to anatomical assessment, as traditional geometric and clinical metrics in cardiac segmentation do not take into account the anatomical correctness of the predicted shapes. The completed study sheds a new light on the ranking of models.
A region and category confidence-based multi-task network for carotid ultrasound image segmentation and classification
Hubei University of Technology（湖北工业大学）；Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属梨园医院）；Zhongnan Hospital, Wuhan University（武汉大学中南医院）；Robarts Research Institute, Western University（西安大略大学罗伯茨研究所）
DeepSPV: A Deep Learning Pipeline for 3D Spleen Volume Estimation from 2D Ultrasound Images
School of Biomedical Engineering and Imaging Science, King’s College London（生物医学工程与成像科学学院，伦敦国王学院）；Department of Biomedical Engineering, National University of Singapore（生物医学工程系，新加坡国立大学）；School of Electronics & Computer Science, University of Southampton（电子与计算机科学学院，南安普顿大学）；Ultromics Ltd（Ultromics有限公司）；Evelina London Children’s Hospital, Guy’s and St Thomas NHS Foundation Trust（埃文琳伦敦儿童医院，盖茨医院及圣 Thomas NHS基金会信托）
MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentation
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University（上海大学先进通信与数据科学研究院智能医学与AI放射技术实验室）；School of Health Science and Engineering, University of Shanghai for Science and Technology（上海科技大学健康科学与工程学院）；Department of Pediatric Cardiology, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院儿童心脏科部）；School of Medical Instruments, Shanghai University of Medicine and Health Sciences（医学仪器学院，上海医学院与健康科学大学）
Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually. Early diagnosis of such cases is of great importance, since it enables clinicians to apply a more effective treatment strategy. This paper introduces an interpretable classification approach of carotid ultrasound images for the risk assessment and stratification of patients with carotid atheromatous plaque. To address the highly imbalanced distribution of patients between the symptomatic and asymptomatic classes (16 vs 58, respectively), an ensemble learning scheme…
Benchmarking Self-Supervised Models for Cardiac Ultrasound View Classification
Department of Systems；Computer Engineering, Carleton University, Ottawa, Ontario, Canada Department of Methodological；Implementation Research, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada Department of Acute Care Research, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada Department of Obstetrics；Gynecology, University of Ottawa, Ottawa, Ontario, Canada School of Epidemiology；Public Health, University of Ottawa, Ottawa, Ontario, Canada Department of Obstetrics, Gynecology \& Newborn Care, The Ottawa Hospital, Ottawa, Ontario, Canada International；Global Health Office, University of Ottawa, Ottawa, Ontario, Canada College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
PAII Inc.（PAII 公司）；Chang Gung Memorial Hospital, Linkou Medical Center（长庚纪念医院林口医学中心）；Institute of Molecular and Genomic Medicine, National Health Research Institutes（国家卫生研究院分子与基因组医学研究所）；Ping An Insurance Group（平安保险集团）
Background & Aims: Hepatic steatosis is a major cause of chronic liver disease. 2D ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective. We developed a scalable deep learning (DL) algorithm for quantitative scoring of liver steatosis from 2D ultrasound images. Approach & Results: Using retrospectively collected multi-view ultrasound data from 3,310 patients, 19,513 studies, and 228,075 images, we trained a DL algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe) from ultrasound diagnoses. Perform…
