Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral located lung cancer lesions. Convolutional Neural Networks (CNNs) are promising tools for medical image analysis but have been performing poorly for cases when existing a significant imbalanced feature distribution, which is true for the airway data as the trachea and principal bronchi dominate most of the voxels whereas the lobar bronchi and distal segmental bronchi occupy a small proportion. In this paper, we propose a Differentiable Topology-Preserved Distance Transform (DTPDT) framework to improve the performance of airway segmentation. A Topology-Preserved Surrogate (TPS) learning strategy is first proposed to balance the training progress within-class distribution. Furthermore, a Convolutional Distance Transform (CDT) is designed to identify the breakage phenomenon with superior sensitivity and minimize the variation of the distance map between the predictionand ground-truth. The proposed method is validated with the publically available reference airway segmentation datasets. The detected rate of branch and length on public EXACT'09 and BAS datasets are 82.1%/79.6% and 96.5%/91.5% respectively, demonstrating the reliability and efficiency of the method in terms of improving the topology completeness of the segmentation performance while maintaining the overall topology accuracy.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
Shanghai Jiao Tong University（上海交通大学）；InferVision Medical Technology Co., Ltd.（推想医疗科技股份有限公司）；University of California, Los Angeles（加州大学洛杉矶分校）；Imperial College London（帝国理工学院）
MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography
School of Electrical and Computer Engineering, Universidade Estadual de Campinas（坎皮纳斯州立大学电气与计算机工程学院）；University of Iowa（爱荷华大学）
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）
Bronchovascular Tree-Guided Weakly Supervised Learning Method for Pulmonary Segment Segmentation
Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院放射科）；Canon Medical Systems (China)（佳能医疗系统（中国））
AirMorph: Topology-Preserving Deep Learning for Pulmonary Airway Analysis
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）；Shanghai Chest Hospital（上海市胸科医院）
Alleviating Class-wise Gradient Imbalance for Pulmonary Airway Segmentation
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Shanghai Chest Hospital（上海市胸科医院）；Shanghai Engineering Research Center of Respiratory Endoscopy（上海市呼吸内镜工程技术研究中心）
Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医学机器人研究所）；School of Computer Science and Technology, Guizhou University（贵州大学计算机科学与技术学院）；Shanghai Chest Hospital, Shanghai Jiao Tong University（上海交通大学附属胸科医院）；Université de Lyon, INSA Lyon, CREATIS, CNRS, INSERM（里昂大学、里昂国立应用科学学院、CREATIS实验室、法国国家科学研究中心、法国国家健康与医学研究院）
PANS: Probabilistic Airway Navigation System for Real-time Robust Bronchoscope Localization
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）；Centre for Artificial Intelligence and Robotics, Chinese Academy of Sciences（中国科学院人工智能与机器人中心）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；Dept. of Radiology, The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院放射科）
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；University of Parma（帕尔马大学）；Shanghai MicroPort MedBot (Group) Co., Ltd.（上海微创医疗机器人（集团）股份有限公司）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Cambridge International Exam Centre in Shanghai Experimental School（上海实验学校剑桥国际考试中心）；InferVision Medical Technology Co., Ltd.（推想医疗科技股份有限公司）；Shanghai MediWorks Precision Instruments Co., Ltd（上海美沃克斯精密仪器有限公司）；Sanmed Biotech Ltd.（三曼生物科技有限公司）；University of Cambridge（剑桥大学）；University of Palermo（巴勒莫大学）；IRCCS Istituto delle Scienze Neurologiche di Bologna（IRCCS博洛尼亚神经科学研究所）；Istituto Italiano di Tecnologia（意大利技术研究院）；University of Oxford（牛津大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；INSA Rouen Normandie（鲁昂国立应用科学学院）；Univ Rouen Normandie（鲁昂诺曼底大学）；Université Le Havre Normandie（勒阿弗尔诺曼底大学）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）；Wake Forest University School of Medicine（维克森林大学医学院）；University College London（伦敦大学学院）；King's College London（伦敦国王学院）
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
Imperial College London（帝国理工学院）；Royal Brompton Hospital（皇家布朗普顿医院）；King's College London（伦敦国王学院）；TECNALIA, Basque Research and Technology Alliance (BRTA)（TECNALIA，巴斯克研究与技术联盟（BRTA））；University of the Basque Country UPV/EHU（巴斯克大学 UPV/EHU）
Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images
Graz University of Technology（格拉茨技术大学）；Ludwig Boltzmann Institute for Lung Vascular Research（路德维希·玻尔兹曼肺血管研究所）；Ludwig Boltzmann Institute for Clinical-Forensic Imaging（路德维希·玻尔兹曼临床法医影像研究所）；Medical University of Graz（格拉茨医科大学）
LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation
Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究所）；Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）
Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images
Northeastern University（东北大学）；Shenyang University of Technology（沈阳工业大学）；The Chinese University of Hong Kong（香港中文大学）；Second Affiliated Hospital of Dalian Medical University（大连医科大学附属第二医院）；Central Hospital Affiliated to Shenyang Medical College（沈阳医学院附属中心医院）
GDDS: Pulmonary Bronchioles Segmentation with Group Deep Dense Supervision
University of Science and Technology of China（中国科学技术大学）；Second Affiliated Hospital, Naval Medical University（海军军医大学第二附属医院）；Suzhou Aerospace Information Research Institute（苏州航天信息研究院）
Coarse-to-fine Airway Segmentation Using Multi information Fusion Network and CNN-based Region Growing
School of Mechanical engineering and Automation, Fuzhou University（福州大学机械工程及自动化学院）；School of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；Thoracic Department, Fujian Medical University Union Hospital（福建医科大学附属协和医院胸外科）
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；University of the Basque Country UPV/EHU（巴斯克大学（UPV/EHU））；TECNALIA, Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟（BRTA）泰克纳利亚研究中心）；Technical University of Munich（慕尼黑工业大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Alberta（阿尔伯塔大学）；The Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Royal Brompton Hospital（皇家布朗普顿医院）
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
The University of Hong Kong（香港大学）；ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University（上海交通大学图像处理与模式识别研究所）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Department of Respiratory and Critical Care Medicine, Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Engineering Research Center of Respiratory Endoscopy（上海市胸科医院呼吸与危重症医学科、呼吸内镜中心、上海市呼吸内镜工程技术研究中心）
