Image segmentation is a fundamental problem in medical image analysis. In recent years, deep neural networks achieve impressive performances on many medical image segmentation tasks by supervised learning on large manually annotated data. However, expert annotations on big medical datasets are tedious, expensive or sometimes unavailable. Weakly supervised learning could reduce the effort for annotation but still required certain amounts of expertise. Recently, deep learning shows a potential to produce more accurate predictions than the original erroneous labels. Inspired by this, we introduce a very weakly supervised learning method, for cystic lesion detection and segmentation in lung CT images, without any manual annotation. Our method works in a self-learning manner, where segmentation generated in previous steps (first by unsupervised segmentation then by neural networks) is used as ground truth for the next level of network learning. Experiments on a cystic lung lesion dataset show that the deep learning could perform better than the initial unsupervised annotation, and progressively improve itself after self-learning.
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
National Institutes of Health (NIH)（美国国立卫生研究院）；The NIAID Integrated Research Facility（NIAID综合研究设施）；University of Pennsylvania（宾夕法尼亚大学）
在临床环境中，对从计算机断层摄影（CT）扫描中准确且快速地提取肺体积的需求仍然很大，因为由于肺的广泛解剖变化和病理的存在，可用的方法不能提供通用的解决方案。手动注释是目前的黄金标准，耗时且经常受到人为偏见的影响。另一方面，当前最先进的全自动肺分割方法由于不能有效地结合用于处理错误分类和实践的人类输入而未能进入临床实践。本文提出了一种用于CT图像的肺注释工具，该工具是交互式的、高效的和鲁棒的。建议的注释工具产生一个“尽可能准确”的模糊连通性图像分割的基础上的初始注释，然后由有效的手动固定的初始提取，如果认为有必要的从业者。为了给用户提供最大的灵活性，我们的注释工具支持三种主要的操作系统（Windows、Linux和Mac OS X）。将我们的免费软件与市售肺分割工具进行比较的定量结果显示，我们的软件具有更高的一致性和精度，具有相当大的潜力来提高常规临床任务的性能。
CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network
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）
Evaluation of Vision Transformers for Multimodal Image Classification: A Case Study on Brain, Lung, and Kidney Tumors
Centro de Tecnologías de la Imagen (CTIM)（图像技术中心）；Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES)（大学信息学与企业与社会研究所）；University of Las Palmas de Gran Canaria（拉斯帕尔马斯德格拉纳达大学）
LGAN: Lung Segmentation in CT Scans Using Generative Adversarial Network
City University of New York（纽约市立大学）；The College of Staten Island（史泰登岛学院）；The City College（城市学院）；Memorial Sloan-Kettering (MSK) Cancer Center（纪念斯隆-凯特琳癌症中心）
Deep Learning-Based Fetal Lung Segmentation from Diffusion-weighted MRI Images and Lung Maturity Evaluation for Fetal Growth Restriction
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK（生物医学工程与成像科学学院，伦敦国王学院）；Elizabeth Garrett Anderson Institute for Women’s Health, University College, London, UK（埃利扎贝塔·加德纳妇女健康研究所，大学学院，伦敦）；University College London Hospital NHS Foundation Trust, London, UK（伦敦大学学院医院国家健康服务信托基金）；Department of Medical Physics and Biomedical Engineering, University College London Hospitals, London, UK（医学物理与生物医学工程系，伦敦大学学院医院）；Department of Radiology, University College London Hospitals NHS Foundation Trust, London, United Kingdom（放射科，伦敦大学学院医院国家健康服务信托基金）
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
Aalborg University（奥尔堡大学）；Eindhoven University of Technology（埃因霍温理工大学）；Erasmus Medical Center（伊拉斯谟医学中心）；Vendsyssel Hospital（文叙瑟尔医院）；Aalborg University Hospital（奥尔堡大学医院）；University of Copenhagen（哥本哈根大学）
Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models
IEO European Institute of Oncology IRCCS（IEO欧洲肿瘤研究所IRCCS）；Technical University of Munich（慕尼黑工业大学）；University of Milan（米兰大学）；Istituto Imaging della Svizzera Italiana (IIMSI)（瑞士意大利语区成像研究所（IIMSI））；Università della Svizzera italiana（瑞士意大利语区大学）；Bern University Hospital（伯尔尼大学医院）；University of Bern（伯尔尼大学）
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
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）
Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images
Nepal Applied Mathematics and Informatics Institute for research (NAAMII)（尼泊尔应用数学与信息学研究所（NAAMII））；Center for Analytical Sciences, Kathmandu Institute of Applied Sciences (KIAS)（加德满都应用科学学院分析科学中心）；Central Department of Chemistry, Tribhuvan University（特里布万大学化学系）
SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching
University of Electronic Science and Technology of China（电子科技大学）；SenseTime Research（商汤科技研究院）；Tongji University（同济大学）；Sichuan University West China Hospital（四川大学华西医院）；Rutgers University（罗格斯大学）
MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification
Computer Science and Engineering Department, NIIT University, Jaipur, India（尼特大学计算机科学与工程系，印度斋普尔）；CSE department, Christ (Deemed to be University), Bangalore, India（基督大学（认定为大学）计算机科学与工程系，印度班加罗尔）；Faculty of Computing, Engineering and the Built Environment, Birmingham City University, UK（伯明翰城市大学计算、工程与建筑环境学院，英国）
Multimodal Interactive Lung Lesion Segmentation: A Framework for Annotating PET/CT Images based on Physiological and Anatomical Cues
HIDSS4Health - Helmholtz Information and Data Science School for Health（亥姆霍兹信息与健康数据科学学院（HIDSS4Health））；Institute for AI in Medicine (IKIM), University Hospital Essen（埃森大学医院医学人工智能研究所（IKIM））
Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients
Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯国民保健服务基金会信托）；King’s College London（伦敦国王学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民保健服务基金会信托）
Automatic airway segmentation from Computed Tomography using robust and efficient 3-D convolutional neural networks
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）；Copenhagen University Hospital（哥本哈根大学医院）；Herlev-Gentofte Hospital（海莱乌-根措夫特医院）
A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field
Click, Predict, Trust: Clinician-in-the-Loop AI Segmentation for Lung Cancer CT-Based Prognosis within the Knowledge-to-Action Framework
BC Cancer Research Institute（BC癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）；Technological Virtual Collaboration (TECVICO Corp.)（技术虚拟协作公司（TECVICO Corp.））；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Islamic Azad University（伊斯兰阿扎德大学）；Motamed Cancer Institute（莫塔梅德癌症研究所）；ACECR（伊朗学术中心教育、文化与研究组织）；University of Isfahan（伊斯法罕大学）；Hamadan University of Medical Sciences（哈马丹医科大学）；Rajaie Cardiovascular Medical and Research Center（拉贾伊心血管医学与研究中心）；Iran University of Medical Science（伊朗医科大学）；Paracelsus Medical University（帕拉塞尔苏斯医科大学）；BC Cancer（BC癌症中心）
ConvPath: A Software Tool for Lung Adenocarcinoma Digital Pathological Image Analysis Aided by Convolutional Neural Network
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences（中国医学科学院国家癌症中心/肿瘤医院）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Using Spatio-Temporal Dual-Stream Network with Self-Supervised Learning for Lung Tumor Classification on Radial Probe Endobronchial Ultrasound Video
National Yang Ming Chiao Tung University（国立阳明交通大学）；National Taiwan University Cancer Center（台湾大学癌症中心）；National Taiwan University Hospital（台湾大学医院）；National Taiwan University Hsin-Chu Hospital（台湾大学新竹医院）
Early Experiences with Crowdsourcing Airway Annotations in Chest CT
Delft University of Technology（代尔夫特理工大学）；Erasmus Medical Center - Sophia Children’s Hospital（伊拉斯谟医学中心-索菲亚儿童医院）；Erasmus Medical Center（伊拉斯谟医学中心）；University of Copenhagen（哥本哈根大学）
Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT
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）
