Automated segmentation of lung abnormalities in computed tomography is an important step for diagnosing and characterizing lung disease. In this work, we improve upon a previous method and propose S-MEDSeg, a deep learning based approach for accurate segmentation of lung lesions in chest CT images. S-MEDSeg combines a pre-trained EfficientNet backbone, bidirectional feature pyramid network, and modern network advancements to achieve improved segmentation performance. A comprehensive ablation study was performed to evaluate the contribution of the proposed network modifications. The results demonstrate modifications introduced in S-MEDSeg significantly improves segmentation performance compared to the baseline approach. The proposed method is applied to an independent dataset of long COVID inpatients to study the effect of post-acute infection vaccination on extent of lung findings. Open-source code, graphical user interface and pip package are available at https://github.com/MICLab-Unicamp/medseg.
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 …
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 …
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…
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…
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…
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 …
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…
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.…
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 …
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…
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…
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…
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. …
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 …
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…
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…
The health and socioeconomic difficulties caused by the COVID-19 pandemic continues to cause enormous tensions around the world. In particular, this extraordinary surge in the number of cases has put considerable strain on health care systems around the world. A critical step in the treatment and management of COVID-19 positive patients is severity assessment, which is challenging even for expert radiologists given the subtleties at different stages of lung disease severity. Motivated by this challenge, we introduce COVID-Net CT-S, a suite of deep convolutional neural networks for predicting l…
KU Leuven（鲁汶大学）；UZ Leuven（鲁汶大学医院）；Vrije Universiteit Brussel (VUB)（布鲁塞尔自由大学）；imec
Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease. There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans. The main tasks of interest are the automatic segmentation of lung and lung lesions in chest CT scans of confirmed or suspected COVID-19 patients. In this study, we compare twelve deep learning algorithms using a multi-center dataset, including both open-source and in…
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…
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…
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…
COVID-19, also known as Novel Coronavirus Disease, is a highly contagious disease that first surfaced in China in late 2019. SARS-CoV-2 is a coronavirus that belongs to the vast family of coronaviruses that causes this disease. The sickness originally appeared in Wuhan, China in December 2019 and quickly spread to over 213 nations, becoming a global pandemic. Fever, dry cough, and tiredness are the most typical COVID-19 symptoms. Aches, pains, and difficulty breathing are some of the other symptoms that patients may face. The majority of these symptoms are indicators of respiratory infections …
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 …
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…
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 …
The First Affiliated Hospital of USTC（中国科学技术大学附属第一医院）；University of Science and Technology of China（中国科学技术大学）；University of Science and Technology（科技大学）；The first affiliated hospital of Bengbu medical college（蚌埠医学院第一附属医院）；AItour（AItour公司）
Background: Triage of patients is important to control the pandemic of coronavirus disease 2019 (COVID-19), especially during the peak of the pandemic when clinical resources become extremely limited. Purpose: To develop a method that automatically segments and quantifies lung and pneumonia lesions with synthetic chest CT and assess disease severity in COVID-19 patients. Materials and Methods: In this study, we incorporated data augmentation to generate synthetic chest CT images using public available datasets (285 datasets from "Lung Nodule Analysis 2016"). The synthetic images and masks were…
COMSATS University Islamabad（伊斯兰堡COMSATS大学）；Bacha Khan University（巴查汗大学）；Chung-Ang University（中央大学）；University of Malaya（马来亚大学）；University Malaysia Sabah（马来西亚沙巴大学）；Institute of Space Technology（空间技术研究所）
The ongoing COVID-19 pandemic has already taken millions of lives and damaged economies across the globe. Most COVID-19 deaths and economic losses are reported from densely crowded cities. It is comprehensible that the effective control and prevention of epidemic/pandemic infectious diseases is vital. According to WHO, testing and diagnosis is the best strategy to control pandemics. Scientists worldwide are attempting to develop various innovative and cost-efficient methods to speed up the testing process. This paper comprehensively evaluates the applicability of the recent top ten state-of-th…
One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the urgency of its implementation. This is the case during the current pandemic of COVID-19. This pathogen primarily infects the respiratory system of the afflicted, resulting in pneumonia and in a severe case of acute respiratory distress syndrome. This results in the formation of different pathological structures in the lungs that can be detected by the use of chest X-rays. Due to the overloa…
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…
Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severity of lung damage and the detection of a COVID-infection using three-dimensional CT-data. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we design and analyze different pretraining methods specifically designed to improve the models ability to handle three-dimensional CT-data. We rank 2nd in the 1st COVID19 Severity Detection Challenge and 3rd in the 2nd COVID19 Detectio…
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…
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,…
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…
The pneumonia caused by Coronavirus disease (COVID-19) is one of major global threat and a number of detection and treatment procedures are suggested by the researchers for COVID-19. The proposed work aims to suggest an automated image processing scheme to extract the COVID-19 lesion from the lung CT scan images (CTI) recorded from the patients. This scheme implements the following procedures; (i) Image pre-processing to enhance the COVID-19 lesions, (ii) Image post-processing to extract the lesions, and (iii) Execution of a relative analysis between the extracted lesion segment and the Ground…
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…
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 …
Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection
School of Computer and Communication Engineering, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；Universidade Federal Fluminense（弗鲁米嫩塞联邦大学）；DASA Complexo Hospitalar de Niterói（达萨尼泰罗伊医院综合体）；Michigan Technological University（密歇根理工大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；Shanghai Institute of Cardiovascular Diseases（上海市心血管病研究所）；National Clinical Research Center for Interventional Medicine（国家介入医学临床研究中心）；Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University（密歇根理工大学生物计算与数字健康中心、计算与网络系统研究所及健康研究所）
University of Science and Technology of China（中国科学技术大学）；The First Affiliated Hospital of USTC (Anhui Provincial Hospital)（中国科学技术大学附属第一医院（安徽省立医院））
COVID-19 has become a global pandemic and is still posing a severe health risk to the public. Accurate and efficient segmentation of pneumonia lesions in CT scans is vital for treatment decision-making. We proposed a novel unsupervised approach using cycle consistent generative adversarial network (cycle-GAN) which automates and accelerates the process of lesion delineation. The workflow includes lung volume segmentation, "synthetic" healthy lung generation, infected and healthy image subtraction, and binary lesion mask creation. The lung volume volume was firstly delineated using a pre-traine…
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…
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…
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 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…
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…
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…
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 …
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…
CAD-Unet: A Capsule Network-Enhanced Unet Architecture for Accurate Segmentation of COVID-19 Lung Infections from CT Images
School of Information Engineering, Ningxia University（宁夏大学信息工程学院）；Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Ningxia University（宁夏人工智能与信息安全重点实验室）；School of Mathematics and Computer Science, Ningxia Normal University（宁夏师范学院数学与计算机科学学院）；School of Advanced Interdisciplinary Studies, Ningxia University（宁夏大学先进交叉学科学院）
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 …
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 …
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）
The new coronavirus infection has shocked the world since early 2020 with its aggressive outbreak. Rapid detection of the disease saves lives, and relying on medical imaging (Computed Tomography and X-ray) to detect infected lungs has shown to be effective. Deep learning and convolutional neural networks have been used for image analysis in this context. However, accurate identification of infected regions has proven challenging for two main reasons. Firstly, the characteristics of infected areas differ in different images. Secondly, insufficient training data makes it challenging to train var…
