Transfer learning (TL) for medical image segmentation helps deep learning models achieve more accurate performances when there are scarce medical images. This study focuses on completing segmentation of the ribs from lung ultrasound images and finding the best TL technique with U-Net, a convolutional neural network for precise and fast image segmentation. Two approaches of TL were used, using a pre-trained VGG16 model to build the U-Net (V-Unet) and pre-training U-Net network with grayscale natural salient object dataset (X-Unet). Visual results and dice coefficients (DICE) of the models were compared. X-Unet showed more accurate and artifact-free visual performances on the actual mask prediction, despite its lower DICE than V-Unet. A partial-frozen network fine-tuning (FT) technique was also applied to X-Unet to compare results between different FT strategies, which FT all layers slightly outperformed freezing part of the network. The effect of dataset sizes was also evaluated, showing the importance of the combination between TL and data augmentation.
Ryerson University（瑞尔森大学）；Mount Sinai Hospital（西奈山医院）；University of Toronto（多伦多大学）
Over the last few decades, Lung Ultrasound (LUS) has been increasingly used to diagnose and monitor different lung diseases in neonates. It is a non invasive tool that allows a fast bedside examination while minimally handling the neonate. Acquiring a LUS scan is easy, but understanding the artifacts concerned with each respiratory disease is challenging. Mixed artifact patterns found in different respiratory diseases may limit LUS readability by the operator. While machine learning (ML), especially deep learning can assist in automated analysis, simply feeding the ultrasound images to an ML m…
Towards Enhanced Analysis of Lung Cancer Lesions in EBUS-TBNA -- A Semi-Supervised Video Object Detection Method
National Yang Ming Chiao Tung University（阳明交通大学）；National Taiwan University Cancer Center（台湾大学癌症中心）；National Taiwan University Hospital（台湾大学医院）；National Taiwan University Hsin-Chu Hospital（台湾大学新竹医院）
Cochin University of Science and Technology（科钦科技大学）；Indian Institute of Technology Palakkad（帕拉卡德印度理工学院）
In the recent past with the rapid surge of COVID-19 infections, lung ultrasound has emerged as a fast and powerful diagnostic tool particularly for continuous and periodic monitoring of the lung. There have been many attempts towards severity classification, segmentation and detection of key landmarks in the lung. Leveraging the progress, an automated lung ultrasound video analysis package is presented in this work, which can provide summary of key frames in the video, flagging of the key frames with lung infection and options to automatically detect and segment the lung landmarks. The integra…
Carnegie Mellon University（卡内基梅隆大学）；Louisiana State University（路易斯安那州立大学）；Cosmeticsurg.net, LLC（Cosmeticsurg.net有限责任公司）
With the onset of the COVID-19 pandemic, ultrasound has emerged as an effective tool for bedside monitoring of patients. Due to this, a large amount of lung ultrasound scans have been made available which can be used for AI based diagnosis and analysis. Several AI-based patient severity scoring models have been proposed that rely on scoring the appearance of the ultrasound scans. AI models are trained using ultrasound-appearance severity scores that are manually labeled based on standardized visual features. We address the challenge of labeling every ultrasound frame in the video clips. Our co…
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）；King’s College London（伦敦国王学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会信托）
Lung ultrasound (LUS) imaging is used to assess lung abnormalities, including the presence of B-line artefacts due to fluid leakage into the lungs caused by a variety of diseases. However, manual detection of these artefacts is challenging. In this paper, we propose a novel methodology to automatically detect and localize B-lines in LUS videos using deep neural networks trained with weak labels. To this end, we combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network and a temporal attention mechanism. Four different models are compared using data from 60 patie…
Washington University School of Medicine（华盛顿大学医学院）
Deep Learning networks have established themselves as providing state of art performance for semantic segmentation. These techniques are widely applied specifically to medical detection, segmentation and classification. The advent of the U-Net based architecture has become particularly popular for this application. In this paper we present the Dense Recurrent Residual Convolutional Neural Network(Dense R2U CNN) which is a synthesis of Recurrent CNN, Residual Network and Dense Convolutional Network based on the U-Net model architecture. The residual unit helps training deeper network, while the…
University of Iowa（爱荷华大学）；Boston University（波士顿大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Milano-Bicocca（米兰比可卡大学）；San Gerardo Hospital（圣杰拉多医院）
The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is proposed, in which both specifically labeled left and right lungs of humans with COPD, and nonspecifically labeled lungs of animals with acute lung injury, were incorporated into training a single neural network. The resulting network is intended for predicting left and right lung regions in humans with or without diffuse opacification and consolidati…
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Cosmetic Surgery Facility LLC（美容外科机构有限责任公司）
We present W-Net, a novel Convolution Neural Network (CNN) framework that employs raw ultrasound waveforms from each A-scan, typically referred to as ultrasound Radio Frequency (RF) data, in addition to the gray ultrasound image to semantically segment and label tissues. Unlike prior work, we seek to label every pixel in the image, without the use of a background class. To the best of our knowledge, this is also the first deep-learning or CNN approach for segmentation that analyses ultrasound raw RF data along with the gray image. International patent(s) pending [PCT/US20/37519]. We chose subc…
Ultrasound Lung Aeration Map via Physics-Aware Neural Operators
California Institute of Technology（加州理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；North Carolina State University（北卡罗来纳州立大学）；NVIDIA（英伟达）
ETH Zurich（苏黎世联邦理工学院）；Clinique des Grangettes（格兰奇诊所）；Biomedical Research Institute August Pi i Sunyer（奥古斯特·皮·苏涅尔生物医学研究所）；Technische Universität München（慕尼黑工业大学）
With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities ar…
University of Florida（佛罗里达大学）；US Anesthesia Partners（美国麻醉伙伴公司）
Automated object recognition in medical images can facilitate medical diagnosis and treatment. In this paper, we automatically segmented supraclavicular nerves in ultrasound images to assist in injecting peripheral nerve blocks. Nerve blocks are generally used for pain treatment after surgery, where ultrasound guidance is used to inject local anesthetics next to target nerves. This treatment blocks the transmission of pain signals to the brain, which can help improve the rate of recovery from surgery and significantly decrease the requirement for postoperative opioids. However, Ultrasound Guid…
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures
National Yang Ming Chiao Tung University（阳明交通大学）；National Taiwan University Cancer Center（台湾大学医学院附设癌医中心医院）；National Taiwan University Hospital（台湾大学医学院附设医院）；National Taiwan University Hsin-Chu Hospital（台湾大学医学院附设新竹医院）
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）深圳大数据研究院）
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annotations of US images, it is difficult to apply Artificial Intelligence (AI) assisting approaches for lung's multi-symptom (multi-label) classification. To overcome these difficulties, we propose a novel semi-supervised Two-Stream Active Learning (TSAL) method to model complicated features and reduce labeling costs in an iterative procedure. The core component of TSAL is the multi-label learning mechanism, in which lab…
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…
Carnegie Mellon University（卡内基梅隆大学）；Louisiana State University（路易斯安那州立大学）；Cosmeticsurg.net, LLC（Cosmeticsurg.net有限责任公司）
Contemporary artificial neural networks (ANN) are trained end-to-end, jointly learning both features and classifiers for the task of interest. Though enormously effective, this paradigm imposes significant costs in assembling annotated task-specific datasets and training large-scale networks. We propose to decouple feature learning from downstream lung ultrasound tasks by introducing an auxiliary pre-task of visual biomarker classification. We demonstrate that one can learn an informative, concise, and interpretable feature space from ultrasound videos by training models for predicting biomark…
Robotics Institute and Department of ECE, Carnegie Mellon University（卡内基梅隆大学机器人研究所与电子与计算机工程系）；Dept. of Pulmonary and Critical Care Medicine, Louisiana State University（路易斯安那州立大学肺病与危重症医学科）；Cosmeticsurg.net, LLC（Cosmeticsurg.net有限责任公司）
In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveals better pleura detail. We compare the diagnostic utility of the pleura and adipose tissue using an HFL ultrasound probe. Masking the adipose tissue during training and inference (while retaining the pleural line and Merlin's space artifacts such as A-lines and B-lines) improved the AI model's diagnostic accuracy.
Eurecat, Centre Tecnològic de Catalunya（加泰罗尼亚技术中心 Eurecat）；Universitat Pompeu Fabra（庞培法布拉大学）；ICREA
The early identification of malignant pulmonary nodules is critical for better lung cancer prognosis and less invasive chemo or radio therapies. Nodule malignancy assessment done by radiologists is extremely useful for planning a preventive intervention but is, unfortunately, a complex, time-consuming and error-prone task. This explains the lack of large datasets containing radiologists malignancy characterization of nodules. In this article, we propose to assess nodule malignancy through 3D convolutional neural networks and to integrate it in an automated end-to-end existing pipeline of lung …
King’s College London（伦敦国王学院）；Oxford University Clinical Research Unit（牛津大学临床研究组）；Mahidol Oxford Research Unit（玛希隆牛津研究组）
Lung ultrasound (LUS) imaging is becoming popular in the intensive care units (ICU) for assessing lung abnormalities such as the appearance of B-line artefacts as a result of severe dengue. These artefacts appear in the LUS images and disappear quickly, making their manual detection very challenging. They also extend radially following the propagation of the sound waves. As a result, we hypothesize that a polar representation may be more adequate for automatic image analysis of these images. This paper presents an attention-based Convolutional+LSTM model to automatically detect B-lines in LUS …
University of Dayton（代顿大学）；St. Jude Children's Research Hospital（圣裘德儿童研究医院）；Microsoft（微软）
3D lung segmentation is essential since it processes the volumetric information of the lungs, removes the unnecessary areas of the scan, and segments the actual area of the lungs in a 3D volume. Recently, the deep learning model, such as U-Net outperforms other network architectures for biomedical image segmentation. In this paper, we propose a novel model, namely, Recurrent Residual 3D U-Net (R2U3D), for the 3D lung segmentation task. In particular, the proposed model integrates 3D convolution into the Recurrent Residual Neural Network based on U-Net. It helps learn spatial dependencies in 3D…
Lung cancer has emerged as a severe disease that threatens human life and health. The precise segmentation of lung regions is a crucial prerequisite for localizing tumors, which can provide accurate information for lung image analysis. In this work, we first propose a lung image segmentation model using the NASNet-Large as an encoder and then followed by a decoder architecture, which is one of the most commonly used architectures in deep learning for image segmentation. The proposed NASNet-Large-decoder architecture can extract high-level information and expand the feature map to recover the s…
Birla Institute of Technology and Science, Pilani（博拉理工学院皮拉尼校区）；Hiroshima University（广岛大学）；Keimyung University Dongsan Medical Center（启明大学东山医疗中心）
Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule identification used complex architecture with large number of parameters. This study developed a cascaded architecture which can accurately segment and classify the benign or malignant lung nodules on computed tomography (CT) images. The main contribution of this study is to introduce a segmentation network where the first stage trained on a public data set can help to recognize the images which included a nodule from an…
The University of Sydney（悉尼大学）；University of California San Diego（加州大学圣地亚哥分校）
Ultrasound is progressing toward becoming an affordable and versatile solution to medical imaging. With the advent of COVID-19 global pandemic, there is a need to fully automate ultrasound imaging as it requires trained operators in close proximity to patients for a long period of time, therefore increasing risk of infection. In this work, we investigate the important yet seldom-studied problem of scan target localization, under the setting of lung ultrasound imaging. We propose a purely vision-based, data driven method that incorporates learning-based computer vision techniques. We combine a …
Ultrasound Image Synthesis Using Generative AI for Lung Ultrasound Detection
Johns Hopkins University（约翰斯·霍普金斯大学）；Philips Ultrasound（飞利浦超声）；Columbia University Vagelos College of Physicians and Surgeons（哥伦比亚大学瓦格洛斯内科医生与外科医生学院）；MedStar Washington Hospital Center（MedStar华盛顿医院中心）；Yale University School of Medicine（耶鲁大学医学院）
Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment. Manual inspection of B-lines requires experts and is time-consuming, whilst modelling automation methods is currently problematic because of a lack of ground truth. Therefore, in this paper, we propose a novel semi-supervised learning method for the B-line detection task based on contrastive learning. Through multi-level unsupervised learning on unlabelled lung ultrasound images, the features of the ar…
Universidad Nacional Mayor de San Marcos（圣马科斯国立大学）；Universidad Nacional de Ingeniería（国立工程大学）
The COVID-19 pandemic started in China in December 2019 and quickly spread to several countries. The consequences of this pandemic are incalculable, causing the death of millions of people and damaging the global economy. To achieve large-scale control of this pandemic, fast tools for detection and treatment of patients are needed. Thus, the demand for alternative tools for the diagnosis of COVID-19 has increased dramatically since accurated and automated tools are not available. In this paper we present the ongoing work on a system for COVID-19 detection using ultrasound imaging and using Dee…
How well do routinely collected demographic and clinical variables aid point-of-care lung ultrasound TB classification
University of Stellenbosch（斯泰伦博斯大学）；Swiss Federal Institute of Technology (EPFL)（瑞士联邦理工学院（EPFL））；National Teaching Hospital for Tuberculosis and Pulmonary Diseases (CNHU-PPC)（国家结核病与肺部疾病教学医院（CNHU-PPC））；South African Medical Research Council（南非医学研究理事会）；Stellenbosch University（斯泰伦博斯大学）
University of Georgia（佐治亚大学）；First Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides an opportunity for designing effective treatment and making financial and care plans. In this paper, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images, which aims to learn a direct mapping from 3D images to class labels. To achieve this goal, four two-pathway Convolutional Neural Networks (CNN) are proposed, including a basic 3D CNN, a novel multi-output network, a 3D DenseNet, and an …
Indian Institute of Technology Palakkad（帕拉卡德印度理工学院）；La Paz Hospital（拉巴斯医院）；Government Medical College Thiruvananthapuram（特里凡得琅医学院）
Lung ultrasound (LUS) is an increasingly popular diagnostic imaging modality for continuous and periodic monitoring of lung infection, given its advantages of non-invasiveness, non-ionizing nature, portability and easy disinfection. The major landmarks assessed by clinicians for triaging using LUS are pleura, A and B lines. There have been many efforts for the automatic detection of these landmarks. However, restricting to a few pre-defined landmarks may not reveal the actual imaging biomarkers particularly in case of new pathologies like COVID-19. Rather, the identification of key landmarks s…
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Louisiana State University（路易斯安那州立大学）；Cosmeticsurg.net, LLC（Cosmeticsurg.net有限责任公司）
We propose using a pre-trained segmentation model to perform diagnostic classification in order to achieve better generalization and interpretability, terming the technique reverse-transfer learning. We present an architecture to convert segmentation models to classification models. We compare and contrast dense vs sparse segmentation labeling and study its impact on diagnostic classification. We compare the performance of U-Net trained with dense and sparse labels to segment A-lines, B-lines, and Pleural lines on a custom dataset of lung ultrasound scans from 4 patients. Our experiments show …
Indian Institute of Technology Palakkad（印度理工学院帕拉卡德分校）；Hospital Universitario Puerta de Hierro（普埃尔塔·德·耶罗大学医院）；Sree Chitra Tirunal Institute for Medical Sciences & Technology（斯里·奇特拉·蒂鲁纳尔医学科学与技术研究所）；Government Medical College Thiruvananthapuram（特里凡得琅政府医学院）
Lung ultrasound (LUS) is possibly the only medical imaging modality which could be used for continuous and periodic monitoring of the lung. This is extremely useful in tracking the lung manifestations either during the onset of lung infection or to track the effect of vaccination on lung as in pandemics such as COVID-19. There have been many attempts in automating the classification of severity of lung into various classes or automatic segmentation of various LUS landmarks and manifestations. However, all these approaches are based on training static machine learning models which require a sig…
University College London（伦敦大学学院）；Frimley Health NHS Foundation Trust（弗林姆利健康国民医疗服务基金会信托）；Royal Berkshire NHS Foundation Trust（皇家伯克郡国民医疗服务基金会信托）；Hospital de La Santa Creu I Sant Pau（圣克鲁和圣保罗医院）；West Suffolk NHS Foundation Trust（西萨福克国民医疗服务基金会信托）；Cambridge University Hospital（剑桥大学医院）；University of Cambridge（剑桥大学）；Great Ormond Street Children's Hospital NHS Foundation Trust（大奥蒙德街儿童医院国民医疗服务基金会信托）；Institute of Child Health, University College London（伦敦大学学院儿童健康研究所）；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）
Lung ultrasound imaging has been shown effective in detecting typical patterns for interstitial pneumonia, as a point-of-care tool for both patients with COVID-19 and other community-acquired pneumonia (CAP). In this work, we focus on the hyperechoic B-line segmentation task. Using deep neural networks, we automatically outline the regions that are indicative of pathology-sensitive artifacts and their associated sonographic patterns. With a real-world data-scarce scenario, we investigate approaches to utilize both COVID-19 and CAP lung ultrasound data to train the networks; comparing fine-tuni…
Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Taiwan University Cancer Center（台湾大学癌症中心）；National Taiwan University Hospital（台湾大学医院）；National Taiwan University Hsin-Chu Hospital（台湾大学新竹医院）
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…
Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis
Philips（飞利浦）；Columbia University Vagelos College of Physicians and Surgeons（哥伦比亚大学瓦格洛斯内科医生与外科医生学院）；MedStar Washington Hospital Center（MedStar华盛顿医院中心）；Yale University School of Medicine（耶鲁大学医学院）
Large annotated lung sound databases are publicly available and might be used to train algorithms for diagnosis systems. However, it might be a challenge to develop a well-performing algorithm for small non-public data, which have only a few subjects and show differences in recording devices and setup. In this paper, we use transfer learning to tackle the mismatch of the recording setup. This allows us to transfer knowledge from one dataset to another dataset for crackle detection in lung sounds. In particular, a single input convolutional neural network (CNN) model is pre-trained on a source …
University of Bristol（布里斯托大学）；CHU Purpan（普尔潘大学中心医院）；IRIT, University of Toulouse（图卢兹大学计算机信息技术研究所）；CNRS（法国国家科学研究中心）
In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Despite being non-convex, the proposed technique is guaranteed to convergence through our proposed Cauchy proximal splitting (CPS) method and accurately identifies both hori…
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…
The state of the art lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection and provides better performance when compared to the current literature. The whole detection pipeline is designed as a single $3D$ Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end ma…
University of Electronic Science and Technology of China（电子科技大学）；Institute of Electronic and Information Engineering of UESTC in Guangdong（电子科技大学广东电子信息工程研究院）；Sichuan Provincial Hospital for Women and Children（四川省妇幼保健院）
The identification of nerve is difficult as structures of nerves are challenging to image and to detect in ultrasound images. Nevertheless, the nerve identification in ultrasound images is a crucial step to improve performance of regional anesthesia. In this paper, a network called Brachial Plexus Multi-instance Segmentation Network (BPMSegNet) is proposed to identify different tissues (nerves, arteries, veins, muscles) in ultrasound images. The BPMSegNet has three novel modules. The first is the spatial local contrast feature, which computes contrast features at different scales. The second o…
Wrong Organ, Right Physics: Transferring Echocardiography Pretraining to Lung Ultrasound for Tuberculosis Screening
University of Stellenbosch（斯泰伦博斯大学）；Swiss Federal Institute of Technology (EPFL)（瑞士联邦理工学院（EPFL））；National Teaching Hospital for Tuberculosis and Pulmonary Diseases (CNHU-PPC)（国家结核病与肺病教学医院（CNHU-PPC））；South African Medical Research Council（南非医学研究理事会）；Stellenbosch University（斯泰伦博斯大学）
The global pandemic of the novel coronavirus disease 2019 (COVID-19) has put tremendous pressure on the medical system. Imaging plays a complementary role in the management of patients with COVID-19. Computed tomography (CT) and chest X-ray (CXR) are the two dominant screening tools. However, difficulty in eliminating the risk of disease transmission, radiation exposure and not being costeffective are some of the challenges for CT and CXR imaging. This fact induces the implementation of lung ultrasound (LUS) for evaluating COVID-19 due to its practical advantages of noninvasiveness, repeatabil…
CPSNet: Physics-Inspired Label-Free Deep Unfolding for Lung Ultrasound B-Line Detection
UCL Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）；Visual Information Laboratory, University of Bristol（布里斯托大学视觉信息实验室）；School of Computer Science and Informatics, Cardiff University（卡迪夫大学计算机科学与信息学院）；Nephrology, Dialysis and Transplantation, Careggi University Hospital（卡尔加奇大学医院肾病、透析与移植科）
提出用于肺部超声图像分析的 CPSNet 无标签深度展开框架，将柯西近端分裂算法展开为网络架构，引入新损失函数，无监督训练，用于 B 线检测时比传统方法更具优势，能有效辅助临床诊断。
Worcester Polytechnic Institute（伍斯特理工学院）；MCPHS University（麻省药科与健康科学大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic of epic proportions and a global response to prepare health systems worldwide is of utmost importance. In addition to its cost-effectiveness in a resources-limited setting, lung ultrasound (LUS) has emerged as a rapid noninvasive imaging tool for the diagnosis of COVID-19 infected patients. Concerns surrounding LUS include the disparity of infected patients and healthcare providers, relatively small number of physicians and sonographers capable of performing LUS, and most importantly, the requirement for s…
Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset compris…
University College London（伦敦大学学院）；Frimley Health NHS Foundation Trust（弗林利健康NHS基金会信托）；Royal Berkshire NHS Foundation Trust（皇家伯克希尔NHS基金会信托）；Hospital de La Santa Creu I Sant Pau（圣十字圣保罗医院）；West Suffolk NHS Foundation Trust（西萨福克NHS基金会信托）；University of Cambridge（剑桥大学）；Great Ormond Street Children's Hospital NHS Foundation Trust（大奥蒙德街儿童医院NHS基金会信托）；University of Pavia（帕维亚大学）
Artificial intelligence-based analysis of lung ultrasound imaging has been demonstrated as an effective technique for rapid diagnostic decision support throughout the COVID-19 pandemic. However, such techniques can require days- or weeks-long training processes and hyper-parameter tuning to develop intelligent deep learning image analysis models. This work focuses on leveraging 'off-the-shelf' pre-trained models as deep feature extractors for scoring disease severity with minimal training time. We propose using pre-trained initializations of existing methods ahead of simple and compact neural …
