Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences（伊斯法罕医科大学医学图像与信号处理研究中心）；Snap Inc.（Snap公司）；ECE Department, Isfahan University of Technology（伊斯法罕理工大学电子与计算机工程系）；Iowa Institute for Biomedical Imaging, The University of Iowa（爱荷华大学生物医学成像研究所）
Narges Saeedizadeh, Shervin Minaee, Rahele Kafieh, Shakib Yazdani, Milan Sonka
The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. As of mid-July 2020, more than 12 million people were infected, and more than 570,000 death were reported. Computed Tomography (CT) images can be used as an alternative to the time-consuming RT-PCR test, to detect COVID-19. In this work we propose a segmentation framework to detect chest regions in CT images, which are infected by COVID-19. We use an architecture similar to U-Net model, and train it to detect ground glass regions, on pixel level. As the infected regions tend to form a connected component (rather than randomly distributed pixels), we add a suitable regularization term to the loss function, to promote connectivity of the segmentation map for COVID-19 pixels. 2D-anisotropic total-variation is used for this purpose, and therefore the proposed model is called "TV-UNet". Through experimental results on a relatively large-scale CT segmentation dataset of around 900 images, we show that adding this new regularization term leads to 2\% gain on overall segmentation performance compared to the U-Net model. Our experimental analysis, ranging from visual evaluation of the predicted segmentation results to quantitative assessment of segmentation performance (precision, recall, Dice score, and mIoU) demonstrated great ability to identify COVID-19 associated regions of the lungs, achieving a mIoU rate of over 99\%, and a Dice score of around 86\%.
A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs
Erasmus MC（伊拉斯姆斯医学中心）；Erasmus MC-Sophia Children Hospital（伊拉斯姆斯医学中心索菲亚儿童医院）；University of Copenhagen（哥本哈根大学）；Herlev-Gentofte Hospital, Copenhagen University Hospital（哥本哈根大学医院海尔鲁夫-根措夫特医院）
COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
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）
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）
OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images
Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与传播科学学院）；Department of Electrical Engineering, Qatar University（卡塔尔大学电气工程学院）
GAEI-UNet: Global Attention and Elastic Interaction U-Net for Vessel Image Segmentation
School of Science Hong Kong University of Science and Technology（香港科技大学理学院）；Department of Mathematics Hong Kong University of Science and Technology（香港科技大学数学系）
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT
Hangzhou City University（杭州城市大学）；Southwest Hospital, Army Medical University (Third Military Medical University)（陆军军医大学（第三军医大学）西南医院）；Zhejiang University（浙江大学）
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）
COVID-19 Infection Segmentation from Chest CT Images Based on Scale Uncertainty
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）
Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
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）
Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Siemens Healthineers（西门子医疗）
Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Stanford University School of Medicine（斯坦福大学医学院）；Algoverse AI Research（Algoverse AI 研究院）
SPEEDNet: Salient Pyramidal Enhancement Encoder-Decoder Network for Colonoscopy Images
Indian Institute of Information Technology (IIIT) Jabalpur（印度贾巴尔普尔信息技术学院）；Gujarat Technological University（古吉拉特理工大学）；Indian Institute of Technology (IIT) Roorkee（印度理工学院鲁尔基分校）；SVIMS Tirupati（蒂鲁帕蒂斯里文卡特斯瓦拉医学科学研究所）
Segmentation of Mental Foramen in Orthopantomographs: A Deep Learning Approach
School of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；University of Essex（埃塞克斯大学）；Faculty of Dental Medicine, University of Airlangga（艾尔朗加大学牙医学院）；Tel Aviv University（特拉维夫大学）；King George’s Medical University（乔治国王医科大学）
Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis
Institute of Technical；Business Information Systems, Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany Human Technopole, Milan, Italy Institute of Medical Engineering, Faculty of Electrical Engineering；Information Technology, Otto von Guericke University Magdeburg, Magdeburg, Germany Centre for Behavioural Brain Sciences, Magdeburg, Germany
Identification of images of COVID-19 from Chest X-rays using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0 Software with Open Source Convolutional Neural Networks
COVID-19大流行对人类造成严重及灾难性影响，并被视为本世纪最严重的健康灾难。检测COVID-19的最佳方法之一是放射学图像，即X射线和计算机断层扫描或CT扫描图像。许多公司和教育机构在这场危机中走到了一起，创建了各种深度学习模型，用于从胸部X光摄影图像中有效诊断COVID-19。例如，滑铁卢大学与Darwin AI一起设计了深度学习模型COVID-Net，并创建了一个名为COVIDx的数据集，由13，975张图像组成。在这项研究中，COGNEX的深度学习软件-VisionPro Deep Learning用于对COVIDx数据集中的这些胸部X射线进行分类。将结果与COVID-Net和开源社区的各种其他最先进的深度学习模型的结果进行比较。深度学习工具通常被称为黑盒，因为人类无法解释模型如何或为什么将图像分类到特定类别。这个问题通过使用两种设置测试VisionPro深度学习来解决，首先选择整个图像，即选择整个图像作为ROI区域，其次在第一步中分割肺部，然后仅对分割的肺部进行分类步骤，而不是使用整个图像。VisionPro深度学习的结果-在作为ROI的整个图像上，它实现了94.0%的总体F分数，在分割的肺部上，它获得了95.3%的F分数，这与COVID-Net和其他最先进的开源深度学习模型相当或更好。
A lightweight Convolutional Neural Network based on U shape structure and Attention Mechanism for Anterior Mediastinum Segmentation
University of Saskatchewan（萨斯喀彻温大学）；Chonnam National University Hwasun Hospital（全南大学和顺医院）；Tarbiat Modares University（塔比亚特·莫达雷斯大学）；National Institute of Technology, Trichy（特里吉国家理工学院）；Jeonbuk National University Medical School（全北国立大学医学院）
Progressive Curriculum Learning with Scale-Enhanced U-Net for Continuous Airway Segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；Centre of AI and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
