We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. The cerebral aneurysms most often occur on a particular structure of the vascular tree named the Circle of Willis. Various studies have been conducted to detect and monitor the aneurysms and those based on Deep Learning achieve the best performance. Specifically, in this work, we propose a full synthetic 3D model able to mimic the brain vasculature as acquired by Magnetic Resonance Angiography, Time Of Flight principle. Among the various MRI modalities, this latter allows for a good rendering of the blood vessels and is non-invasive. Our model has been designed to simultaneously mimic the arteries' geometry, the aneurysm shape, and the background noise. The vascular tree geometry is modeled thanks to an interpolation with 3D Spline functions, and the statistical properties of the background noise is collected from angiography acquisitions and reproduced within the model. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, finally, we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.
Rethinking Intracranial Aneurysm Vessel Segmentation: A Perspective from Computational Fluid Dynamics Applications
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；Department of Civil and Environmental Engineering, Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Huashan Hospital, Fudan University（复旦大学华山医院）
Uppsala University（乌普萨拉大学）；Cornell University（康奈尔大学）；Cornell Tech（康奈尔科技学院）；Weill Cornell Medicine（威尔康奈尔医学院）；Radiological Society of North America（北美放射学会）；Taipei Medical University Hospital（台北医学大学附属医院）；UC San Diego（加州大学圣地亚哥分校）；Prince Sultan Military Medical City（苏丹王子军事医疗城）；Liverpool Hospital（利物浦医院）；Clinical Center of the University of Sarajevo（萨拉热窝大学临床中心）；Memorial University of Newfoundland（纽芬兰纪念大学）；Fleni（弗莱尼医院）；University of California San Francisco（加州大学旧金山分校）；Koç University School of Medicine（科奇大学医学院）；Intermed IUHW Hospital（国际医疗福祉大学医院）；Hacettepe University（哈塞特佩大学）；Duke University（杜克大学）；China Medical University Hospital（中国医药大学附属医院）
Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm Hemodynamics
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）
SynVA: A Modular Toolkit for Vessel Generation and Aneurysm Editing
Visual Computing and Artificial Intelligence, Kiel University, Germany（视觉计算与人工智能研究所，基尔大学，德国）；Institute for Medical Informatics and Statistics, Kiel University, Germany（医学信息学与统计研究所，基尔大学，德国）；Clinic for Neuroradiology, Medical Faculty, Magdeburg University, Germany（神经放射科，马格德堡大学医学学院，德国）；Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Germany（放射学与神经放射学部门，石勒苏益格-荷尔斯泰因大学医院，德国）；Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland（数学与计算机科学学院，亚当·密茨凯维奇大学，波兰）
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）
Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
King’s College London（伦敦国王学院）；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）；University College London Hospital NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；King’s College Hospital（国王学院医院）
Weakly Supervised Intracranial Aneurysm Detection and Segmentation in MR angiography via Multi-task UNet with Vesselness Prior
Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada（计算机科学与软件工程系，康科迪亚大学）；NeuroRx Research, Montréal, Canada（NeuroRx研究公司）；Department of Electrical and Computer Engineering, Concordia University, Montréal, Canada（电气与计算机工程系，康科迪亚大学）
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）
Beyond Augmentation: Cross-Modal Transformer Fusion with Bi-directional Attention for Low-Data Aneurysm Screening
Carnegie Mellon University（卡内基梅隆大学）；Artificial Intelligence in Medicine (AIM) Program（医学人工智能（AIM）项目）；Mass General Brigham（马萨诸塞总医院）；Harvard Medical School（哈佛医学院）；Department of Radiation Oncology（放射肿瘤科）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Brigham and Women’s Hospital（布里洛妇产科医院）
Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces
Department of Applied Mathematics, Technical Medical Centre, University of Twente（应用数学系、技术医学中心、埃因霍温理工大学）；Department of Surgery, Amsterdam University medical center, Location University of Amsterdam（外科系、阿姆斯特丹大学医学中心、阿姆斯特丹大学）；Department of Surgery, Amsterdam University medical center, location Vrije Universiteit Amsterdam（外科系、阿姆斯特丹大学医学中心、自由大学）；Amsterdam Cardiovascular Sciences, Atherosclerosis and Aortic Diseases（阿姆斯特丹心血管科学、动脉粥样硬化和腹主动脉瘤疾病）
Anatomy-Informed Deep Learning for Abdominal Aortic Aneurysm Segmentation
Faculty of Mathematics, Informatics and Technology, University of Applied Sciences Koblenz（科布伦茨应用科学大学数学、信息学与技术学院）；Department of Radiology, Herz-Jesu-Krankenhaus（赫茨-耶苏医院放射科）
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）
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）
