Lung infection and normal region segmentation from CT volumes of COVID-19 cases
Nagoya University（名古屋大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；National Institute of Informatics（国立信息学研究所）；Keio University School of Medicine（庆应义塾大学医学部）；Juntendo University（顺天堂大学）
This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes of COVID-19 patients. From December 2019, novel coronavirus disease 2019 (COVID-19) spreads over the world and giving significant impacts to our economic activities and daily lives. To diagnose the large number of infected patients, diagnosis assistance by computers is needed. Chest CT is effective for diagnosis of viral pneumonia including COVID-19. A quantitative analysis method of condition of the lung from CT volumes by computers is required for diagnosis assistance of COVID-19. This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes using a COVID-19 segmentation fully convolutional network (FCN). In diagnosis of lung diseases including COVID-19, analysis of conditions of normal and infection regions in the lung is important. Our method recognizes and segments lung normal and infection regions in CT volumes. To segment infection regions that have various shapes and sizes, we introduced dense pooling connections and dilated convolutions in our FCN. We applied the proposed method to CT volumes of COVID-19 cases. From mild to severe cases of COVID-19, the proposed method correctly segmented normal and infection regions in the lung. Dice scores of normal and infection regions were 0.911 and 0.753, respectively.
Saratoga High School（萨拉托加高中）；Stanford University（斯坦福大学）
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models. Most work to date assessing disease severity on chest imaging has focused on segmenting computed tomography (CT) images; however, given that CTs are performed much less frequently than chest X-rays for COVID-19 patients, automated lung lesion segmentation on chest X-rays could be clinically valuable. There currently exists a universal shortage of chest X-rays with ground truth COVID-19 lung le…
Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection …
University of Technology Sydney（悉尼科技大学）；Mehr General Hospital（Mehr综合医院）；Charles Sturt University（查尔斯特大学）
The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focusing on diagnosis and stratification of COVID-19 from medical images. Despite this large-scale research effort, these models have found limited practical application due in part to unproven generalization of these models beyond their source study. This study investigates the generalizability of key published models using the publicly available COVID-19 Computed Tomography data through cross dataset validation. We then assess the predictive ability of these models for COVID-19 s…
Melbourne Data Analytics Platform（墨尔本数据分析平台）；University of Melbourne（墨尔本大学）
Improving automated analysis of medical imaging will provide clinicians more options in providing care for patients. The 2023 AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides an opportunity to test and refine machine learning methods for detecting the presence and severity of COVID-19 in patients from CT scans. This paper presents version 2 of Cov3d, a deep learning model submitted in the 2022 competition. The model has been improved through a preprocessing step which segments the lungs in the CT scan and crops the input to this region. It …
Inception Institute of Artificial Intelligence（人工智能研究院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the traini…
With the ongoing COVID-19 pandemic, understanding the characteristics of the virus has become an important and challenging task in the scientific community. While tests do exist for COVID-19, the goal of our research is to explore other methods of identifying infected individuals. Our group applied unsupervised clustering techniques to explore a dataset of lungscans of COVID-19 infected, Viral Pneumonia infected, and healthy individuals. This is an important area to explore as COVID-19 is a novel disease that is currently being studied in detail. Our methodology explores the potential that uns…
The coronavirus disease (COVID-19) has resulted in a pandemic crippling the a breadth of services critical to daily life. Segmentation of lung infections in computerized tomography (CT) slices could be be used to improve diagnosis and understanding of COVID-19 in patients. Deep learning systems lack interpretability because of their black box nature. Inspired by human communication of complex ideas through language, we propose a symbolic framework based on emergent languages for the segmentation of COVID-19 infections in CT scans of lungs. We model the cooperation between two artificial agents…
The novelty of the COVID-19 disease and the speed of spread has created a colossal chaos, impulse among researchers worldwide to exploit all the resources and capabilities to understand and analyze characteristics of the coronavirus in term of the ways it spreads and virus incubation time. For that, the existing medical features like CT and X-ray images are used. For example, CT-scan images can be used for the detection of lung infection. But the challenges of these features such as the quality of the image and infection characteristics limitate the effectiveness of these features. Using artif…
Ben-Gurion University（本-古里安大学）；Soroka Medical Center（索罗卡医疗中心）
We present a novel deep learning approach to categorical segmentation of lung CTs of COVID-19 patients. Specifically, we partition the scans into healthy lung tissues, non-lung regions, and two different, yet visually similar, pathological lung tissues, namely, ground-glass opacity and consolidation. This is accomplished via a unique, end-to-end hierarchical network architecture and ensemble learning, which contribute to the segmentation and provide a measure for segmentation uncertainty. The proposed framework achieves competitive results and outstanding generalization capabilities for three …
Nanjing University of Science and Technology（南京理工大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；China Electronics Cloud Brain (Tianjin) Technology CO., LTD（中电云脑（天津）科技有限公司）；Jiangsu University of Technology（江苏理工学院）；Fudan University（复旦大学）；University of Toronto（多伦多大学）；Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School（南京大学医学院附属鼓楼医院）；Lenovo Ltd.（联想集团）；Nanjing University（南京大学）
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods: To promote the development of data-efficient deep …
Shahid Sadoughi University of Medical Sciences（沙希德·萨杜吉医科大学）；Mashhad University of Medical Sciences（马什哈德医科大学）；Geneva University Hospital（日内瓦大学医院）
This study aimed to evaluate the performance of a novel unsupervised deep learning-based framework for automated infections lesion segmentation from CT images of Covid patients. In the first step, two residual networks were independently trained to identify the lung tissue for normal and Covid patients in a supervised manner. These two models, referred to as DL-Covid and DL-Norm for Covid-19 and normal patients, respectively, generate the voxel-wise probability maps for lung tissue identification. To detect Covid lesions, the CT image of the Covid patient is processed by the DL-Covid and DL-No…
Seoul National University Hospital（首尔大学医院）；Massachusetts General Hospital（麻省总医院）；Kyungpook National University（庆北国立大学）；Yeungnam University College of Medicine（岭南大学医学院）；Catholic University of Daegu School of Medicine（大邱加图立大学医学院）；Keimyung University School of Medicine（启明大学医学院）
Purpose. Imaging plays an important role in assessing severity of COVID 19 pneumonia. However, semantic interpretation of chest radiography (CXR) findings does not include quantitative description of radiographic opacities. Most current AI assisted CXR image analysis framework do not quantify for regional variations of disease. To address these, we proposed a four region lung segmentation method to assist accurate quantification of COVID 19 pneumonia. Methods. A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, wh…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）
Scarcity of annotated images hampers the building of automated solution for reliable COVID-19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein present a label-free approach for segmenting COVID-19 lesions in CT via pixel-level anomaly modeling that mines out the relevant knowledge from normal CT lung scans. Our modeling is inspired by the observation that the parts of tracheae and vessels, which lay in the high-intensity range where lesions belong to, exhibit strong patterns. To facilitate the learning of such patterns at a pixel level, we synthesize `les…
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…
Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination. We propose a novel approach, called DRR4Covid, to learn automated COVID-19 diagnosis and infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid comprises of an infection-aware DRR generator, a classification and/or segmentation network, and a domain adaptation module. The infection-aware DRR generator is able to produce DRRs with adjustable strength of radiological signs of COVID-19 infection, and generate pixel-level infection annota…
Darmstadt University of Technology（达姆施塔特工业大学）；University Hospital Frankfurt（法兰克福大学医院）
Automatic segmentation of lung lesions in computer tomography has the potential to ease the burden of clinicians during the Covid-19 pandemic. Yet predictive deep learning models are not trusted in the clinical routine due to failing silently in out-of-distribution (OOD) data. We propose a lightweight OOD detection method that exploits the Mahalanobis distance in the feature space. The proposed approach can be seamlessly integrated into state-of-the-art segmentation pipelines without requiring changes in model architecture or training procedure, and can therefore be used to assess the suitabil…
Nanjing University（南京大学）；National Institute of Healthcare Data Science at Nanjing University（南京大学医疗健康数据科学国家研究院）；Central South University（中南大学）；Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；University of North Carolina（北卡罗来纳大学）
Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessm…
Beihang University（北京航空航天大学）；Taiyuan University of Technology（太原理工大学）；Hefei Innovation Research Institute, Beihang University（北京航空航天大学合肥创新研究院）；Beijing Advanced Innovation Centre for Biomedical Engineering（北京生物医学工程高精尖创新中心）；Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine（北京大数据精准医疗高精尖创新中心）
The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relati…
Zhejiang University（浙江大学）；the First Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；Hangzhou AiSmartVision Co., Ltd.（杭州智视人工智能有限公司）
Utilizing computed tomography (CT) images to quickly estimate the severity of cases with COVID-19 is one of the most straightforward and efficacious methods. Two tasks were studied in this present paper. One was to segment the mask of intact lung in case of pneumonia. Another was to generate the masks of regions infected by COVID-19. The masks of these two parts of images then were converted to corresponding volumes to calculate the physical proportion of infected region of lung. A total of 129 CT image set were herein collected and studied. The intrinsic Hounsfiled value of CT images was firs…
Sberbank AI Laboratory（俄罗斯联邦储蓄银行人工智能实验室）；New York University（纽约大学）
Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19.Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizingpatients by severity of the disease. In this paper we adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of slices of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.…
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 coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. Due to rising skepticism towards the sensitivity of RT-PCR as screening method, medical imaging like computed tomography offers great potential as alternative. For this reason, automated image segmentation is highly desired as clinical decision support for quantitative assessment and disease monitoring. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches. To address this problem, we propose an innovat…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；National Institute of Technology Delhi（印度德里国家技术学院）
Background and Objective: Artificial intelligence (AI) methods coupled with biomedical analysis has a critical role during pandemics as it helps to release the overwhelming pressure from healthcare systems and physicians. As the ongoing COVID-19 crisis worsens in countries having dense populations and inadequate testing kits like Brazil and India, radiological imaging can act as an important diagnostic tool to accurately classify covid-19 patients and prescribe the necessary treatment in due time. With this motivation, we present our study based on deep learning architecture for detecting covi…
Isfahan University of Technology（伊斯法罕理工大学）；Seattle University（西雅图大学）；McMaster University（麦克马斯特大学）
Coronavirus has caused hundreds of thousands of deaths. Fatalities could decrease if every patient could get suitable treatment by the healthcare system. Machine learning, especially computer vision methods based on deep learning, can help healthcare professionals diagnose and treat COVID-19 infected cases more efficiently. Hence, infected patients can get better service from the healthcare system and decrease the number of deaths caused by the coronavirus. This research proposes a method for segmenting infected lung regions in a CT image. For this purpose, a convolutional neural network with …
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 Engineering and Applied Science (UEAS)（工程与应用科学大学）
COVID-19 is a new pathogen that first appeared in the human population at the end of 2019, and it can lead to novel variants of pneumonia after infection. COVID-19 is a rapidly spreading infectious disease that infects humans faster. Therefore, efficient diagnostic systems may accurately identify infected patients and thus help control their spread. In this regard, a new two-stage analysis framework is developed to analyze minute irregularities of COVID-19 infection. A novel detection Convolutional Neural Network (CNN), STM-BRNet, is developed that incorporates the Split-Transform-Merge (STM) …
Shahid Sadoughi University of Medical Sciences（沙希德·萨杜基医科大学）；Mashhad University of Medical Sciences（马什哈德医科大学）；Geneva University Hospital（日内瓦大学医院）
Automated semantic image segmentation is an essential step in quantitative image analysis and disease diagnosis. This study investigates the performance of a deep learning-based model for lung segmentation from CT images for normal and COVID-19 patients. Chest CT images and corresponding lung masks of 1200 confirmed COVID-19 cases were used for training a residual neural network. The reference lung masks were generated through semi-automated/manual segmentation of the CT images. The performance of the model was evaluated on two distinct external test datasets including 120 normal and COVID-19 …
The worldwide spread of coronavirus disease (COVID-19) has become a threatening risk for global public health. It is of great importance to rapidly and accurately screen patients with COVID-19 from community acquired pneumonia (CAP). In this study, a total of 1658 patients with COVID-19 and 1027 patients of CAP underwent thin-section CT. All images were preprocessed to obtain the segmentations of both infections and lung fields, which were used to extract location-specific features. An infection Size Aware Random Forest method (iSARF) was proposed, in which subjects were automated categorized …
An early effective screening and grading of COVID-19 has become imperative towards optimizing the limited available resources of the medical facilities. An automated segmentation of the infected volumes in lung CT is expected to significantly aid in the diagnosis and care of patients. However, an accurate demarcation of lesions remains problematic due to their irregular structure and location(s) within the lung. A novel deep learning architecture, Composite Deep network with Feature Weighting (CDNetFW), is proposed for efficient delineation of infected regions from lung CT images. Initially a …
Nagoya University（名古屋大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；National Institute of Informatics（国立信息学研究所）；Keio University School of Medicine（庆应义塾大学医学部）；Juntendo University（顺天堂大学）
This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and deaths. CT image-based diagnosis of COVID-19 can provide quick and accurate diagnosis results. An automated segmentation method of infection regions in the lung provides a quantitative criterion for diagnosis. Previous methods employ whole 2D image or 3D volume-based processes. Infection regions have a considerable variation in their sizes. Such processes easily miss small infection regions. Patch-based process is effecti…
This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data. Most of the studies (n=42) used GANs for data augmen…
Shanghai Public Health Clinical Center, Fudan University（复旦大学附属公共卫生临床中心）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai University（上海大学）
CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported that bilateral and peripheral ground glass opacification (GGO) with or without consolidation are predominant CT findings in COVID-19 patients. However, due to lack of computerized quantification tools, only qualitative impression and rough description of infected areas are currently used in radiological reports. In this paper, a deep learning (DL)-based segmentation system is developed to automatically quantify infect…
Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a mass of studies for lung infection segmentation from CT images. Though many methods have been proposed for this issue, it is a challenging task because of infections of various size appearing in different lobe zones. To tackle these issues, we propose a Graph-based Pyramid Global Context Reasoning (Graph-PGCR) module, which is capable of modeling long-range dependencies among disjoint infections as well as adapt size variation. We first incorporate graph convolution to exploit long-term contextual information from multi…
Universidade Estadual de Maringá（马里兰州立大学）；Instituto Federal do Paraná（巴拉那联邦学院）；Universidade Tecnológica Federal do Paraná（巴拉那联邦理工大学）；Universidade Federal do Paraná（巴拉那联邦大学）；Università Degli Studi di Padova（帕多瓦大学）；Universidade Federal de Pernambuco（伯南布哥联邦大学）
COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three…
University of Michigan（密歇根大学）；Henry Ford Health System（亨利·福特医疗系统）；Molecular Medicine Institute（分子医学研究所）
CXR-Net is a two-module Artificial Intelligence pipeline for the quick detection of SARS-CoV-2 from chest X-rays (CXRs). Module 1 was trained on a public dataset of 6395 CXRs with radiologist annotated lung contours to generate masks of the lungs that overlap the heart and large vasa. Module 2 is a hybrid convnet in which the first convolutional layer with learned coefficients is replaced by a layer with fixed coefficients provided by the Wavelet Scattering Transform (WST). Module 2 takes as inputs the patients CXRs and corresponding lung masks calculated by Module 1, and produces as outputs a…
Darmstadt University of Technology（达姆施塔特工业大学）；Uniklinik Frankfurt（法兰克福大学医院）
Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty …
Beijing Jiaotong University（北京交通大学）；Beijing Technology and Business University（北京工商大学）；City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）
The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. Thanks to the development of deep learning technology, some deep learning solutions for lung infection segmentation have been proposed. However, due to the scattered distribution, complex background interference and blurred boundaries, t…
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…
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…
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
Tampere University（坦佩雷大学）；Qatar University（卡塔尔大学）；Reem Medical Center（里姆医疗中心）；Hamad Medical Corporation Hospital（哈马德医疗公司医院）；Weill Cornell Medicine - Qatar（威尔康奈尔医学院卡塔尔分校）
University of Waterloo（滑铁卢大学）；DarwinAI Corp.（DarwinAI公司）；Stony Brook School of Medicine（石溪大学医学院）；Montefiore Medical Center（蒙蒂菲奥里医疗中心）；Albert Einstein College of Medicine（阿尔伯特·爱因斯坦医学院）
Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the COVID-19 pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Materials and Methods: Data consisted of 396 CXRs fro…
Beijing Jiaotong University（北京交通大学）；Beijing Technology and Business University（北京工商大学）；City University of Hong Kong（香港城市大学）；Ningbo University（宁波大学）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Peng Cheng Laboratory（鹏城实验室）；Huawei Technologies（华为技术有限公司）
The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows a…
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…
St. Joseph’s College of Engineering（圣约瑟夫工程学院）；Techno India College of Technology（泰克诺印度技术学院）；Shantou University（汕头大学）；Cairo University（开罗大学）；University of South Dakota（南达科他大学）
Pneumonia is one of the foremost lung diseases and untreated pneumonia will lead to serious threats for all age groups. The proposed work aims to extract and evaluate the Coronavirus disease (COVID-19) caused pneumonia infection in lung using CT scans. We propose an image-assisted system to extract COVID-19 infected sections from lung CT scans (coronal view). It includes following steps: (i) Threshold filter to extract the lung region by eliminating possible artifacts; (ii) Image enhancement using Harmony-Search-Optimization and Otsu thresholding; (iii) Image segmentation to extract infected r…
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）
Since 2019, coronavirus Disease 2019 (COVID-19) has been widely spread and posed a serious threat to public health. Chest Computed Tomography (CT) holds great potential for screening and diagnosis of this disease. The segmentation of COVID-19 CT imaging can achieves quantitative evaluation of infections and tracks disease progression. COVID-19 infections are characterized by high heterogeneity and unclear boundaries, so capturing low-level features such as texture and intensity is critical for segmentation. However, segmentation networks that emphasize low-level features are still lacking. In …
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…
University of Engineering and Applied Science（工程与应用科学大学）
In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high structural similarity between infection and background. In this regard, a new two-phase deep convolutional neural network (CNN) based diagnostic system is proposed to detect minute irregularities and analyze COVID-19 infection. In the first phase, a novel SB-STM-BRNet CNN is deve…
Pakistan Institute of Engineering & Applied Sciences（巴基斯坦工程与应用科学学院）；Catholic University of Daegu（大邱天主教大学）
COVID-19 is a global health problem. Consequently, early detection and analysis of the infection patterns are crucial for controlling infection spread as well as devising a treatment plan. This work proposes a two-stage deep Convolutional Neural Networks (CNNs) based framework for delineation of COVID-19 infected regions in Lung CT images. In the first stage, initially, COVID-19 specific CT image features are enhanced using a two-level discrete wavelet transformation. These enhanced CT images are then classified using the proposed custom-made deep CoV-CTNet. In the second stage, the CT images …
Stony Brook University（石溪大学）；Stony Brook University Hospital（石溪大学医院）
Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. However, comprehensive visualization systems focused on supporting the dual visual+DL diagnosis of COVID-19 are non-existent. We present COVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. The system incorporates a complete pipeline of automatic lungs segmentation, localization/ isolation of lung abnormalities, followed by visualization, visual and DL analysis, and measuremen…
National Research Council of Italy（意大利国家研究委员会）；University Paris-Est Creteil（巴黎东部克雷泰伊大学）
In the last three years, the world has been facing a global crisis caused by Covid-19 pandemic. Medical imaging has been playing a crucial role in the fighting against this disease and saving the human lives. Indeed, CT-scans has proved their efficiency in diagnosing, detecting, and following-up the Covid-19 infection. In this paper, we propose a new Transformer-CNN based approach for Covid-19 infection segmentation from the CT slices. The proposed D-TrAttUnet architecture has an Encoder-Decoder structure, where compound Transformer-CNN encoder and Dual-Decoders are proposed. The Transformer-C…
College of Engineering and Technology, American University of the Middle East（中东美国大学工程与技术学院）
Coronavirus Disease spread globally and infected millions of people quickly, causing high pressure on the health-system facilities. PCR screening is the adopted diagnostic testing method for COVID-19 detection. However, PCR is criticized due its low sensitivity ratios, also, it is time-consuming and manual complicated process. CT imaging proved its ability to detect the disease even for asymptotic patients, which make it a trustworthy alternative for PCR. In addition, the appearance of COVID-19 infections in CT slices, offers high potential to support in disease evolution monitoring using auto…
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…
Concordia University（康考迪亚大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）；University of Toronto（多伦多大学）；McGill University（麦吉尔大学）
Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) scans can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensiv…
Zhengzhou University（郑州大学）；Ningbo Huamei Hospital, University of the Chinese Academy of Sciences（中国科学院大学宁波华美医院）
The spread of the novel coronavirus disease 2019 (COVID-19) has claimed millions of lives. Automatic segmentation of lesions from CT images can assist doctors with screening, treatment, and monitoring. However, accurate segmentation of lesions from CT images can be very challenging due to data and model limitations. Recently, Transformer-based networks have attracted a lot of attention in the area of computer vision, as Transformer outperforms CNN at a bunch of tasks. In this work, we propose a novel network structure that combines CNN and Transformer for the segmentation of COVID-19 lesions. …
Università degli Studi di Milano（米兰大学）；ASST Grande Ospedale Metropolitano Niguarda（ASST 大都会大医院尼瓜达）；INFN Sezione di Milano（意大利国家核物理研究院米兰分部）
Purpose: to optimize a pipeline of clinical data gathering and CT images processing implemented during the COVID-19 pandemic crisis and to develop artificial intelligence model for different of viral pneumonia. Methods: 1028 chest CT image of patients with positive swab were segmented automatically for lung extraction. A Gaussian model developed in Python language was applied to calculate quantitative metrics (QM) describing well-aerated and ill portions of the lungs from the histogram distribution of lung CT numbers in both lungs of each image and in four geometrical subdivision. Furthermore,…
