Weakly Semi-Supervised Detection in Lung Ultrasound Videos
Jiahong Ouyang, Li Chen, Gary Y. Li, Naveen Balaraju, Shubham Patil, Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Kenton W. Gregory, Cynthia R. Gregory, Me… 展开作者
Jiahong Ouyang, Li Chen, Gary Y. Li, Naveen Balaraju, Shubham Patil, Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Kenton W. Gregory, Cynthia R. Gregory, Meihua Zhu, David O. Kessler, Laurie Malia, Almaz Dessie, Joni Rabiner, Di Coneybeare, Bo Shopsin, Andrew Hersh, Cristian Madar, Jeffrey Shupp, Laura S. Johnson, Jacob Avila, Kristin Dwyer, Peter Weimersheimer, Balasundar Raju, Jochen Kruecker, Alvin Chen
Frame-by-frame annotation of bounding boxes by clinical experts is often required to train fully supervised object detection models on medical video data. We propose a method for improving object detection in medical videos through weak supervision from video-level labels. More concretely, we aggregate individual detection predictions into video-level predictions and extend a teacher-student training strategy to provide additional supervision via a video-level loss. We also introduce improvements to the underlying teacher-student framework, including methods to improve the quality of pseudo-labels based on weak supervision and adaptive schemes to optimize knowledge transfer between the student and teacher networks. We apply this approach to the clinically important task of detecting lung consolidations (seen in respiratory infections such as COVID-19 pneumonia) in medical ultrasound videos. Experiments reveal that our framework improves detection accuracy and robustness compared to baseline semi-supervised models, and improves efficiency in data and annotation usage.
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…
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…
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。
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）
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…
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）
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.
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 …
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 …
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…
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）
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…
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）；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…
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…
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…
Imperial College London（帝国理工学院）；The University of Hong Kong（香港大学）
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 …
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…
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…
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…
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 …
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）
National Research Council Canada（加拿大国家研究委员会）；University of Waterloo（滑铁卢大学）
Besides vaccination, as an effective way to mitigate the further spread of COVID-19, fast and accurate screening of individuals to test for the disease is yet necessary to ensure public health safety. We propose COVID-Net UV, an end-to-end hybrid spatio-temporal deep neural network architecture, to detect COVID-19 infection from lung point-of-care ultrasound videos captured by convex transducers. COVID-Net UV comprises a convolutional neural network that extracts spatial features and a recurrent neural network that learns temporal dependence. After careful hyperparameter tuning, the network ac…
Indian Institute of Technology Palakkad（印度理工学院帕拉卡德分校）；University of Alberta（阿尔伯塔大学）；Hospital Universitario Puerta de Hierro（铁门大学医院）；Government Medical College Thiruvananthapuram（特里凡得琅政府医学院）；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）
Most applications of deep learning techniques in medical imaging are supervised and require a large number of labeled data which is expensive and requires many hours of careful annotation by experts. In this paper, we propose an unsupervised, physics driven domain specific transporter framework with an attention mechanism to identify relevant key points with applications in ultrasound imaging. The proposed framework identifies key points that provide a concise geometric representation highlighting regions with high structural variation in ultrasound videos. We incorporate physics driven domain…
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）
Indian Institute of Technology（印度理工学院）；University of Alberta（阿尔伯塔大学）；Hospital Universitario Puerta de Hierro（普埃塔德耶罗大学医院）；Government Medical College（政府医学院）；Sree Chitra Tirunal Institute of Medical Sciences and Technology（斯里奇特拉图鲁纳尔医学科学与技术研究所）；Bhagwan Mahaveer Jain Hospital（巴格万马哈维尔耆那医院）
The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. The proposed video-summarization technique is a step in this direction that provides clinicians access to relevant key-frames from a given ultrasound scan (such as lung ultrasound) while reducing resource, storage and bandwidth requirements. We propose a new unsupervised reinforcement learning (RL) framework with novel rewards that facilitates unsupervised learning avoiding tedious and impractical manual labelling for summarizing ultr…
Dept. of Electrical Eng. and Information Technology, University of Naples Federico II（那不勒斯费德里科二世大学电气工程与信息技术系）；Dept. of Industrial Engineering, University of Naples Federico II（那不勒斯费德里科二世大学工业工程系）；Division of Neonatology, Section of Pediatrics, Dept. of Translational Medical Sciences, University of Naples Federico II（那不勒斯费德里科二世大学转化医学科学系儿科新生儿科）；Hospital Careggi of Florence（佛罗伦萨卡雷吉医院）；Hospital “Vittore Buzzi”（维托雷·布uzzi医院）；Civil Hospital of Brescia（布雷西亚市民医院）
Lung ultrasound imaging is reaching growing interest from the scientific community. On one side, thanks to its harmlessness and high descriptive power, this kind of diagnostic imaging has been largely adopted in sensitive applications, like the diagnosis and follow-up of preterm newborns in neonatal intensive care units. On the other side, state-of-the-art image analysis and pattern recognition approaches have recently proven their ability to fully exploit the rich information contained in these data, making them attractive for the research community. In this work, we present a thorough analys…
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 new coronavirus 2019 (COVID-2019) has rapidly become a pandemic and has had a devastating effect on both everyday life, public health and the global economy. It is critical to detect positive cases as early as possible to prevent the further spread of this epidemic and to treat affected patients quickly. The need for auxiliary diagnostic tools has increased as accurate automated tool kits are not available. This paper presents a work in progress that proposes the analysis of images of lung ultrasound scans using a convolutional neural network. The trained model will be used on a Raspberry …
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）
Tsinghua University（清华大学）；Capital Medical University（首都医科大学）；Beijing Ditan Hospital（北京地坛医院）
This paper proposes a semi-automatic system based on quantitative characterization of the specific image patterns in lung ultrasound (LUS) images, in order to assess the lung conditions of patients with COVID-19 pneumonia, as well as to differentiate between the severe / and no-severe cases. Specifically, four parameters are extracted from each LUS image, namely the thickness (TPL) and roughness (RPL) of the pleural line, and the accumulated with (AWBL) and acoustic coefficient (ACBL) of B lines. 27 patients are enrolled in this study, which are grouped into 13 moderate patients, 7 severe pati…
Korea University（高丽大学）；Hankik University of Foreign Studies（韩国外国语大学）
We propose a method for effectively utilizing weakly annotated image data in an object detection tasks of breast ultrasound images. Given the problem setting where a small, strongly annotated dataset and a large, weakly annotated dataset with no bounding box information are available, training an object detection model becomes a non-trivial problem. We suggest a controlled weight for handling the effect of weakly annotated images in a two stage object detection model. We~also present a subsequent active learning scheme for safely assigning weakly annotated images a strong annotation using the …
Ultrasound image quality has continually been improving. However, when needles or other metallic objects are operating inside the tissue, the resulting reverberation artifacts can severely corrupt the surrounding image quality. Such effects are challenging for existing computer vision algorithms for medical image analysis. Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees. The boundaries of such artifacts are ambiguous, leading to disagreement among human experts labeling the artifacts. We propose a weakly- and semi-supervised,…
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD). For CT specifically, interpreting the performance of CAD algorithms can be challenging given the large number of co-occurring diseases. This paper examines the effect of co-occurring diseases when training classification models by weakly supervised learning, specifically by comparing multi-label and multiple binary classifiers using the same training data. Our results demonstrated that the binary model outperform…
Beyond Point Annotation: A Weakly Supervised Network Guided by Multi-Level Labels Generated from Four-Point Annotation for Thyroid Nodule Segmentation in Ultrasound Image
Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China（机器人科学与工程学院，东北大学，沈阳，中国）；Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada（生物医学工程系，萨斯喀彻温大学，萨斯喀彻温，加拿大）；Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China（超声科，中国医科大学附属盛京医院，沈阳，中国）
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）
Shenzhen University（深圳大学）；Shenzhen RayShape Medical Technology Co., Ltd（深圳瑞影医疗科技有限公司）；Huizhou Central People’s Hospital（惠州市中心人民医院）
Ultrasound (US) is widely used for its advantages of real-time imaging, radiation-free and portability. In clinical practice, analysis and diagnosis often rely on US sequences rather than a single image to obtain dynamic anatomical information. This is challenging for novices to learn because practicing with adequate videos from patients is clinically unpractical. In this paper, we propose a novel framework to synthesize high-fidelity US videos. Specifically, the synthesis videos are generated by animating source content images based on the motion of given driving videos. Our highlights are th…
VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos
Dept. of Biomedical Informatics \& Data Science, Yale University, New Haven, USA Yale Biomedical Imaging Institute, Yale University, New Haven, USA Dept. of Biomedical Engineering, University of Basel, Allschwil, Switzerland Dept. of Radiology \& Biomedical Imaging, Yale University, New Haven, USA Dept. of Biomedical Engineering, Yale University, New Haven, USA
The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound
Cheriton School of Computer Science, University of Waterloo（滑铁卢大学计算机科学学院）；Department of Systems Design Engineering, University of Waterloo（滑铁卢大学系统设计工程系）；Schulich School of Medicine and Dentistry, Western University（西部大学施乐希医学与牙科学校）
Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）；Guilin University of Electronic Technology（桂林电子科技大学）；Guangdong Provicial People’s Hospital Ganzhou Hospital（广东省人民医院赣州医院）
Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outs…
Indian Institute of Technology, Delhi（德里印度理工学院）；Postgraduate Institute of Medical Education and Research（医学教育与研究研究生院）
Rich temporal information and variations in viewpoints make video data an attractive choice for learning image representations using unsupervised contrastive learning (UCL) techniques. State-of-the-art (SOTA) contrastive learning techniques consider frames within a video as positives in the embedding space, whereas the frames from other videos are considered negatives. We observe that unlike multiple views of an object in natural scene videos, an Ultrasound (US) video captures different 2D slices of an organ. Hence, there is almost no similarity between the temporally distant frames of even th…
Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to disinfect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very clear. Here we train deep neural networks to significantly enhance the capability to detect, grade and mo…
