Automatically labeling intracranial arteries (ICA) with their anatomical names is beneficial for feature extraction and detailed analysis of intracranial vascular structures. There are significant variations in the ICA due to natural and pathological causes, making it challenging for automated labeling. However, the existing public dataset for evaluation of anatomical labeling is limited. We construct a comprehensive dataset with 729 Magnetic Resonance Angiography scans and propose a Graph Neural Network (GNN) method to label arteries by classifying types of nodes and edges in an attributed relational graph. In addition, a hierarchical refinement framework is developed for further improving the GNN outputs to incorporate structural and relational knowledge about the ICA. Our method achieved a node labeling accuracy of 97.5%, and 63.8% of scans were correctly labeled for all Circle of Willis nodes, on a testing set of 105 scans with both healthy and diseased subjects. This is a significant improvement over available state-of-the-art methods. Automatic artery labeling is promising to minimize manual effort in characterizing the complicated ICA networks and provides valuable information for the identification of geometric risk factors of vascular disease. Our code and dataset are available at https://github.com/clatfd/GNN-ARTLABEL.
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；School of Aeronautics and Astronautics, Dalian University of Technology（大连理工大学航空航天学院）；Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院介入神经放射科）；Ultrasonic Department, The Fourth Hospital of Hebei Medical University and Hebei Tumor Hospital（河北医科大学第四医院暨河北省肿瘤医院超声科）；School of Computer Science and Information Security, Guilin University of Electronic Technology（桂林电子科技大学计算机科学与信息安全学院）；Department of Radiology, The Netherlands Cancer Institute（荷兰癌症研究所放射科）；Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical（伊拉斯姆斯大学医学中心放射与核医学科）
AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms
Michigan Technological University（密歇根理工大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Medical University of South Carolina（南卡罗来纳医科大学）；Chang Bing Show Chwan Memorial Hospital（彰滨秀传纪念医院）
ArteryX: A Reliable End-to-End Toolbox for Standardized Intracranial Artery Feature Extraction from 3D TOF-MRA
Department of Neurology, University of Rochester（罗切斯特大学神经科部）；Department of Physics, University of Rochester（罗切斯特大学物理系）；Department of Imaging Sciences, University of Rochester（罗切斯特大学影像科学系）；Department of Electrical & Computer Engineering, University of Rochester（罗切斯特大学电气与计算机工程系）；Department of Biomedical Engineering, University of Rochester（罗切斯特大学生物医学工程系）
Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling
Michigan Technological University（密歇根理工大学）；Medical University of South Carolina（南卡罗来纳医科大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
Coronary Artery Semantic Labeling using Edge Attention Graph Matching Network
Michigan Technological University（密歇根理工大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Chang Bing Show Chwan Memorial Hospital（彰滨秀传纪念医院）
Multi-graph Graph Matching for Coronary Artery Semantic Labeling
Kennesaw State University（肯尼索州立大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Michigan Technological University（密歇根理工大学）；Medical University of South Carolina（南卡罗来纳医科大学）
Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Dept. of Molecular Medicine and Surgery, Karolinska Institutet（卡罗林斯卡研究所分子医学与外科部）；Biomedical Imaging Center, Pontificia Universidad Católica de Chile（智利天主教大学生物医学成像中心）；Department of Electrical Engineering, School of Engineering, Pontificia Universidad Católica de Chile（智利天主教大学工程学院电气工程系）；Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile（智利智能健康工程研究院（iHEALTH））；Department of Medical Physics, Institute of Physics, University of Greifswald（格里夫斯瓦尔德大学物理研究所医学物理系）；Department of Radiology, Northwestern University（西北大学放射科）；Department of Neurological Surgery, Northwestern University（西北大学神经外科部）；Department of Neurology, Northwestern University（西北大学神经科）
Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Brigham and Women’s Hospital（布列根和妇女医院）；Harvard Medical School（哈佛医学院）；Northeastern University（东北大学）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Washing University in St. Louis（圣路易斯华盛顿大学）；First Affiliated Hospital of Guangzhou Medical University（广州医科大学附属第一医院）
Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography
Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院高级医学计算与分析中心）；Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院戈登医学影像中心）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院放射科）
Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
Brigham and Women’s Hospital Harvard Medical School（哈佛医学院布莱尔妇女医院）；Khoury College of Computer Sciences Northeastern University（东北大学计算机科学学院）；Washington University in St. Louis（圣路易斯华盛顿大学）；Dana-Farber Cancer Institute Harvard Medical School（哈佛医学院丹娜-法伯癌症研究院）
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Johns Hopkins University（约翰斯·霍普金斯大学）；Erasmus MC（伊拉斯姆斯大学医学中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；University of Copenhagen（哥本哈根大学）；Technische Universität München（慕尼黑工业大学）
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
University of Bournemouth（伯恩茅斯大学）；IDIAP Research Institute（IDIAP研究所）；École de Technolgie Superieure de Montréal（蒙特利尔高等技术学院）；Diagnos Inc.（Diagnos公司）；CRCHUM University of Montreal Hospital Centre（蒙特利尔大学医院研究中心）
3D CT-Based Coronary Calcium Assessment: A Feature-Driven Machine Learning Framework
Visual Intelligence Lab, School of Computer Science, University of Galway, Ireland（加拉维大学计算机科学学院视觉智能实验室）；Data Science Institute, University of Galway, Ireland（加拉维大学数据科学研究所）；Sharif Cardiovascular Research Group, University of Galway, Ireland（加拉维大学沙里夫心血管研究组）；Department of Cardiology, Galway University Hospital, Galway, Ireland（加拉维大学医院心内科部门）；CRFG, University of Galway, Galway, Ireland（加拉维大学CRFG研究中心）；CURAM, University of Galway, Galway, Ireland（加拉维大学CURAM研究中心）；Cardiology Department, NEOM Hospital, Saudi Arabia（沙特NEOM医院心内科部门）
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT
Center for Research in Computer Vision (CRCV), University of Central Florida（中佛罗里达大学计算机视觉研究中心）；Diagnostic Radiology Department, Florida Hospital（佛罗里达医院放射诊断科）
