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 - a Sender and a Receiver. These agents synergistically cooperate using emergent symbolic language to solve the task of semantic segmentation. Our game theoretic approach is to model the cooperation between agents unlike Generative Adversarial Networks (GANs). The Sender retrieves information from one of the higher layers of the deep network and generates a symbolic sentence sampled from a categorical distribution of vocabularies. The Receiver ingests the stream of symbols and cogenerates the segmentation mask. A private emergent language is developed that forms the communication channel used to describe the task of segmentation of COVID infections. We augment existing state of the art semantic segmentation architectures with our symbolic generator to form symbolic segmentation models. Our symbolic segmentation framework achieves state of the art performance for segmentation of lung infections caused by COVID-19. Our results show direct interpretation of symbolic sentences to discriminate between normal and infected regions, infection morphology and image characteristics. We show state of the art results for segmentation of COVID-19 lung infections in CT.
Kyung Hee University（庆熙大学）；Sharif University of Technology（谢里夫理工大学）；Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease burden and even help predict the outcome. The availability of longitudinal CT series may also result in an efficient and effective method to reliably assess the progression of COVID-19, monitor the healing process and the response to different therapeutic strategies. In this paper, we propose a new framework to identify infection at…
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 …
Beijing Jingzhen Medical Technology Ltd.（北京精诊医疗科技有限公司）；Tsinghua University（清华大学）；Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Xidian University（西安电子科技大学）
A novel coronavirus disease 2019 (COVID-19) was detected and has spread rapidly across various countries around the world since the end of the year 2019, Computed Tomography (CT) images have been used as a crucial alternative to the time-consuming RT-PCR test. However, pure manual segmentation of CT images faces a serious challenge with the increase of suspected cases, resulting in urgent requirements for accurate and automatic segmentation of COVID-19 infections. Unfortunately, since the imaging characteristics of the COVID-19 infection are diverse and similar to the backgrounds, existing med…
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…
Lung nodules can be an alarming precursor to potential lung cancer. Missed nodule detections during chest radiograph analysis remains a common challenge among thoracic radiologists. In this work, we present a multi-task lung nodule detection algorithm for chest radiograph analysis. Unlike past approaches, our algorithm predicts a global-level label indicating nodule presence along with local-level labels predicting nodule locations using a Dual Head Network (DHN). We demonstrate the favorable nodule detection performance that our multi-task formulation yields in comparison to conventional meth…
Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography
Medical University of Warsaw（华沙医科大学）；Warsaw University of Technology（华沙理工大学）；MI2.ai；University of Amsterdam（阿姆斯特丹大学）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Medical University of Silesia（西里西亚医科大学）；University of Warsaw（华沙大学）
Wayne State Univ.（韦恩州立大学）；NanoBioScience Institute, Wayne State Univ.（韦恩州立大学纳米生物科学研究所）；University of Michigan（密歇根大学）；Henry Ford Health System（亨利福特医疗系统）
Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical images. Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show that Res-CR-Net, a new type of fully convolutional neural network, which was originally developed for the semantic segmentation of microscopy images, and which does not adopt a U-Net architecture, is very effective at segmenting the lung fields in chest X-rays from either healthy patients or patients with a variety of lung pathologies.
Kamnoetvidya Science Academy（甘烹碧科学学院）；National Electronics and Computer Technology Center（泰国国家电子和计算机技术中心）；Vidyasirimedhi Institute of Science and Technology（瓦莱拉克科学技术研究院）
Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide more medical information than normal chest x-rays, there is very limited access to these technologies in rural areas. Recently, there is a trend in using computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. In this study, the 121-layer convolutional neural network also known as DenseNet-121 by G. Huang et. al., along with the tra…
University of Technology Sydney（悉尼科技大学）；IBM Australia Limited（IBM澳大利亚有限公司）；Sejong University（世宗大学）；Universiti Kebangsaan Malaysia（马来西亚国民大学）；Charles Sturt University（查尔斯特大学）
Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the black-box nature of deep learning models. Additionally, most lung nodules visible on chest X-ray are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both con…
Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. In this study, we investigate the performance of several deep-learning approaches for automated measurement of total lung volume from chest radiographs. 7621 posteroanterior and lateral view chest radiographs (CXR) were collected from patients with chest CT available. Similarly, 928 CXR studies were chosen from patients with pulmonary function test (PFT) results. The reference total lung volume was calculated from lung segmentation on CT or PFT data, respectively. This dataset …
Bharti School of Telecommunications Technology & Management, Indian Institute of Technology Delhi（印度理工学院德里分校巴蒂电信技术与管理学院）；Teleradiology Solutions（远程放射学解决方案公司）；CSIR-Institute of Genomics and Integrative Biology（CSIR基因组学与整合生物学研究所）；Christian Medical College, Vellore（韦洛尔基督教医学院）
A chest radiograph, commonly called chest x-ray (CxR), plays a vital role in the diagnosis of various lung diseases, such as lung cancer, tuberculosis, pneumonia, and many more. Automated segmentation of the lungs is an important step to design a computer-aided diagnostic tool for examination of a CxR. Precise lung segmentation is considered extremely challenging because of variance in the shape of the lung caused by health issues, age, and gender. The proposed work investigates the use of an efficient deep convolutional neural network for accurate segmentation of lungs from CxR. We attempt an…
National University of Sciences and Technology（国立科技大学）
An essential stage in computer aided diagnosis of chest X rays is automated lung segmentation. Due to rib cages and the unique modalities of each persons lungs, it is essential to construct an effective automated lung segmentation model. This paper presents a reliable model for the segmentation of lungs in chest radiographs. Our model overcomes the challenges by learning to ignore unimportant areas in the source Chest Radiograph and emphasize important features for lung segmentation. We evaluate our model on public datasets, Montgomery and Shenzhen. The proposed model has a DICE coefficient of…
Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN) by utilizing a progressive GAN and a convolutional encoder-decoder-encoder pipeline. Model training uses only unlabelled healthy lung patches extracted from the Indiana University Chest X-Ray Collection. External validation and testing are performed using healthy and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for Radio…
Automated segmentation of Lungs plays a crucial role in the computer-aided diagnosis of chest X-Ray (CXR) images. Developing an efficient Lung segmentation model is challenging because of difficulties such as the presence of several edges at the rib cage and clavicle, inconsistent lung shape among different individuals, and the appearance of the lung apex. In this paper, we propose a robust model for Lung segmentation in Chest Radiographs. Our model learns to ignore the irrelevant regions in an input Chest Radiograph while highlighting regions useful for lung segmentation. The proposed model i…
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…
Kyoto University Graduate School of Medicine（京都大学医学研究科）
Rationale and objectives: Several studies have evaluated the usefulness of deep learning for lung segmentation using chest x-ray (CXR) images with small- or medium-sized abnormal findings. Here, we built a database including both CXR images with severe abnormalities and experts' lung segmentation results, and aimed to evaluate our network's efficacy in lung segmentation from these images. Materials and Methods: For lung segmentation, CXR images from the Japanese Society of Radiological Technology (JSRT, N = 247) and Montgomery databases (N = 138), were included, and 65 additional images depict…
The abundance of overlapping anatomical structures appearing in chest radiographs can reduce the performance of lung pathology detection by automated algorithms (CAD) as well as the human reader. In this paper, we present a deep learning based image processing technique for enhancing the contrast of soft lung structures in chest radiographs using Fully Convolutional Neural Networks (FCNN). Two 2D FCNN architectures were trained to accomplish the task: The first performs 2D lung segmentation which is used for normalization of the lung area. The second FCNN is trained to extract lung structures.…
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
Technical University of Munich（慕尼黑工业大学）；Munich Institute for Biomedical Engineering, Technical University of Munich（慕尼黑工业大学慕尼黑生物医学工程研究所）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；TUM University Hospital（慕尼黑工业大学大学医院）；Philips Innovative Technologies（飞利浦创新技术公司）；Institute for Advanced Study, Technical University of Munich（慕尼黑工业大学高等研究院）
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…
AI Research Group, Institute of Mathematics（人工智能研究组，数学研究所）；Eötvös Loránd University（罗兰大学）
Chest X-ray is the most common test among medical imaging modalities. It is applied for detection and differentiation of, among others, lung cancer, tuberculosis, and pneumonia, the last with importance due to the COVID-19 disease. Integrating computer-aided detection methods into the radiologist diagnostic pipeline, greatly reduces the doctors' workload, increasing reliability and quantitative analysis. Here we present a novel deep learning approach for lung segmentation, a basic, but arduous task in the diagnostic pipeline. Our method uses state-of-the-art fully convolutional neural networks…
PURPOSE: This study aimed to develop a deep learning-based tool to detect and localize lung nodules with chest radiographs(CXRs). We expected it to enhance the efficiency of interpreting CXRs and reduce the possibilities of delayed diagnosis of lung cancer. MATERIALS AND METHODS: We collected CXRs from NCKUH database and VBD, an open-source medical image dataset, as our training and validation data. A number of CXRs from the Ministry of Health and Welfare(MOHW) database served as our test data. We built a segmentation model to identify lung areas from CXRs, and sliced them into 16 patches. Phy…
National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Ping An Insurance Company of China（中国平安保险（集团）股份有限公司）
This paper proposes a novel framework for lung segmentation in chest X-rays. It consists of two key contributions, a criss-cross attention based segmentation network and radiorealistic chest X-ray image synthesis (i.e. a synthesized radiograph that appears anatomically realistic) for data augmentation. The criss-cross attention modules capture rich global contextual information in both horizontal and vertical directions for all the pixels thus facilitating accurate lung segmentation. To reduce the manual annotation burden and to train a robust lung segmentor that can be adapted to pathological…
University of Iowa（爱荷华大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Utah（犹他大学）
Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs increased radiation dose, time, and cost, and may not be possible in many cases, thus restricting the applicability of registration-based biomechanics. We propose a generative adversarial learning approach for estimating local tissue expansion directly from a single CT scan. The proposed framework was trained and evaluated on 2500 subjects from the SPIROMICS cohort. Once trained, the framework can be used as a registr…
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.…
New York University（纽约大学）；Manipal Institute of Technology, Manipal Academy of Higher Education（马尼帕尔高等教育学院马尼帕尔理工学院）；Columbia University（哥伦比亚大学）
Biomedical image segmentation is one of the fastest growing fields which has seen extensive automation through the use of Artificial Intelligence. This has enabled widespread adoption of accurate techniques to expedite the screening and diagnostic processes which would otherwise take several days to finalize. In this paper, we present an end-to-end pipeline to segment lungs from chest X-ray images, training the neural network model on the Japanese Society of Radiological Technology (JSRT) dataset, using UNet to enable faster processing of initial screening for various lung disorders. The pipel…
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…
As the COVID-19 pandemic aggravated the excessive workload of doctors globally, the demand for computer aided methods in medical imaging analysis increased even further. Such tools can result in more robust diagnostic pipelines which are less prone to human errors. In our paper, we present a deep neural network to which we refer to as Attention BCDU-Net, and apply it to the task of lung and heart segmentation from chest X-ray (CXR) images, a basic but ardous step in the diagnostic pipeline, for instance for the detection of cardiomegaly. We show that the fine-tuned model exceeds previous state…
Stony Brook University（石溪大学）；Newark Beth Israel Medical Center（纽瓦克贝斯以色列医疗中心）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医疗中心）
Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire due to the high cost of annotation. Thus, it is desirable to learn a robust and transferable representation in an unsupervised manner to benefit tasks that lack labeled data. Unlike natural images, medical images have their own domain prior; e.g., we observe that many pulmonary diseases, such as the COVID-19, manifest as changes in the lung tissue texture rather than the anatomical structure. Therefore, we hypothesize that studying only the texture without the influence of structure variations would be advantageous for …
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…
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 …
LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance
Eurecat, Centre Tecnològic de Catalunya（埃鲁卡特技术中心，加泰罗尼亚技术中心）；Dept. de Matemàtiques i Informàtica, Universitat de Barcelona（巴塞罗那大学数学与计算机系）；Barcelona Supercomputing Center (BSC)（巴塞罗那超级计算中心）
Harbin Institute of Technology（哈尔滨工业大学）；Southern University of Science and Technology（南方科技大学）；CVTE Research（视源研究院）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；University of Hong Kong（香港大学）；Guangdong Armed Police Hospital（广东武警医院）
Chest X-rays are the most commonly available and affordable radiological examination for screening thoracic diseases. According to the domain knowledge of screening chest X-rays, the pathological information usually lay on the lung and heart regions. However, it is costly to acquire region-level annotation in practice, and model training mainly relies on image-level class labels in a weakly supervised manner, which is highly challenging for computer-aided chest X-ray screening. To address this issue, some methods have been proposed recently to identify local regions containing pathological inf…
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…
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 …
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…
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…
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…
University of Copenhagen（哥本哈根大学）；Cerebriu AS（Cerebriu公司）；Technical University of Denmark（丹麦技术大学）
Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for dat…
School of Professional Studies（专业研究学院）；Northwestern University（西北大学）
Artificial intelligence (AI) is disrupting the medical field as advances in modern technology allow common household computers to learn anatomical and pathological features that distinguish between healthy and disease with the accuracy of highly specialized, trained physicians. Computer vision AI applications use medical imaging, such as lung chest X-Rays (LCXRs), to facilitate diagnoses by providing second-opinions in addition to a physician's or radiologist's interpretation. Considering the advent of the current Coronavirus disease (COVID-19) pandemic, LCXRs may provide rapid insights to ind…
Shanghai Jiao Tong University（上海交通大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；ShanghaiTech University（上海科技大学）
Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. However, it requires large-scale and diverse medical data with high-quality annotations to train such robust and accurate CADs. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation. Nevertheless, previous methods lack the ability to generate nodules that are realistic with the size attribute desired by the …
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…
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…
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…
Silesian University of Technology（西里西亚理工大学）；Medical University of Silesia（西里西亚医科大学）；Voivodship Specialist Hospital（省专科医院）；Central Clinical Hospital of the Ministry of Internal Affairs and Administration（内务与行政部中央临床医院）；Jagiellonian University Medical College（雅盖隆大学医学院）；Kujawsko-Pomorskie Pulmonology Center（库亚维-波美拉尼亚肺病中心）；Silesian Hospital（西里西亚医院）；Medical University of Warsaw（华沙医科大学）；Nicolaus Copernicus University（尼古拉斯·哥白尼大学）；Medical University of Bialystok（比亚韦斯托克医科大学）；Wroclaw Medical University（弗罗茨瓦夫医科大学）；Czerniakowski Hospital（切尔尼亚科夫斯基医院）；MEGREZ Hospital（MEGREZ医院）；Medical University of Gdansk（格但斯克医科大学）；Yale School of Medicine（耶鲁大学医学院）；The Maria Sklodowska-Curie National Research Institute of Oncology（玛丽·斯克沃多夫斯卡-居里国家肿瘤研究所）
The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients w…
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…
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 …
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…
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…
Suppression of thoracic bone shadows on chest X-rays (CXRs) has been indicated to improve the diagnosis of pulmonary disease. Previous approaches can be categorized as unsupervised physical and supervised deep learning models. Nevertheless, with physical models able to preserve morphological details but at the cost of extremely long processing time, existing DL methods face challenges of gathering sufficient/qualitative ground truth (GT) for robust training, thus leading to failure in maintaining clinically acceptable false positive rates. We hereby propose a generalizable yet efficient workfl…
Recent works show that Generative Adversarial Networks (GANs) can be successfully applied to chest X-ray data augmentation for lung disease recognition. However, the implausible and distorted pathology features generated from the less than perfect generator may lead to wrong clinical decisions. Why not keep the original pathology region? We proposed a novel approach that allows our generative model to generate high quality plausible images that contain undistorted pathology areas. The main idea is to design a training scheme based on an image-to-image translation network to introduce variation…
