Deep learning has been shown to produce state of the art results in many tasks in biomedical imaging, especially in segmentation. Moreover, segmentation of the cerebrovascular structure from magnetic resonance angiography is a challenging problem because its complex geometry and topology have a large inter-patient variability. Therefore, in this work, we present a convolutional neural network approach for this problem. Particularly, a new network topology inspired by the U-net 3D and by the Inception modules, entitled Uception. In addition, a discussion about the best objective function for sparse data also guided most choices during the project. State of the art models are also implemented for a comparison purpose and final results show that the proposed architecture has the best performance in this particular context.
Guidelines for Cerebrovascular Segmentation: Managing Imperfect Annotations in the context of Semi-Supervised Learning
Université de Reims Champagne Ardenne（兰斯-香槟-阿登大学）；Univ Lyon（里昂大学）；INSA-Lyon（里昂国立应用科学学院）；Universite Claude Bernard Lyon 1（里昂第一大学）；IMT Atlantique（大西洋电信学院）；Inserm（法国国家健康与医学研究院）
Efficiency vs. Efficacy: Assessing the Compression Ratio-Dice Score Relationship through a Simple Benchmarking Framework for Cerebrovascular 3D Segmentation
Multimedia Interaction and Communication Lab（多媒体交互与通信实验室）；Arab Academy for Science and Technology（阿拉伯科学与技术学院）；Department of Systems and Biomedical Engineering（系统与生物医学工程系）；Cairo University（开罗大学）；Wearables, Biosensing, and Biosignal Processing Research lab（可穿戴设备、生物传感与生物信号处理研究实验室）
XAI-Driven Diagnosis of Generalization Failure in State-Space Cerebrovascular Segmentation Models: A Case Study on Domain Shift Between RSNA and TopCoW Datasets
Department of Electronics and Electrical Communications Engineering（电子与电气通信工程系）；Cairo University（开罗大学）；Multimedia Interaction and Communication Lab（多媒体交互与通信实验室）；Arab Academy for Science and Technology（阿拉伯科学与技术学院）；Wearables, Biosensing, and Biosignal Processing Research lab（可穿戴设备、生物传感与生物信号处理研究实验室）
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）；Leibniz Institute for Neurobiology（莱布尼茨神经生物学研究所）
Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models
Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Mie University Graduate School of Medicine（三重大学医学院）；Mie Chuo Medical Center（三重中央医疗中心）
Segmentation method for cerebral blood vessels from MRA using hysteresis
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；University of Nottingham（诺丁汉大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
EURECOM（欧洲通信学院）；University of Siena（锡耶纳大学）；Centre Hospitalier Universitaire de Nice（尼斯大学医院中心）；Centre for Medical Image Computing University College London（伦敦大学学院医学影像计算中心）；e-health Center Universitat Oberta de Catalunya（加泰罗尼亚开放大学电子健康中心）；Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗救助机构）
An automated framework for brain vessel centerline extraction from CTA images
Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所）；Department of Radiology, Erasmus MC（伊拉斯姆斯大学医学中心放射科）；Department of Radiology, Maastricht UMC（马斯特里赫特大学医学中心放射科）；Faculty of Applied Sciences, Delft University of Technology（代尔夫特理工大学应用科学学院）
Hessian-Based Lightweight Neural Network HessNet for State-of-the-Art Brain Vessel Segmentation on a Minimal Training Dataset
Institute of Artificial Intelligence, M.V.Lomonosov Moscow State University（人工智能研究所，莫斯科国立大学）；Laboratory of Analysis of Semantics, Centre for Language and Semantics Technologies, Faculty of Computer Science, HSE University（语义分析实验室，语言与语义技术中心，计算机科学系，高等经济大学）；Neurosputnik LLC（Neurosputnik公司）；Mechanics and Mathematics Faculty, M.V.Lomonosov Moscow State University（力学与数学系，莫斯科国立大学）；International Laboratory of Algebraic Topology and Its Applications, Faculty of Computer Science, HSE University（国际拓扑学及其应用实验室，计算机科学系，高等经济大学）
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）
Multi-Domain Brain Vessel Segmentation Through Feature Disentanglement
Data Science Department, EURECOM（EURECOM数据科学部门）；Department of Medicine, Surgery and Neuroscience, University of Siena（锡耶纳大学医学、外科与神经科学系）；e-Health Center, Universitat Oberta de Catalunya（加泰罗尼亚开放大学e-健康中心）；Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院医学影像计算中心）；Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London（伦敦大学学院Queen Square多发性硬化中心）；Sorbonne Université, Paris Brain Institute, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière（巴黎大学巴黎脑研究所）；School of Biomedical Engineering & Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）
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）
Vessel-CAPTCHA: an efficient learning framework for vessel annotation and segmentation
EURECOM（法国通信系统工程师学校与研究中心）；Universitat de Barcelona（巴塞罗那大学）；University College London（伦敦大学学院）；Politecnico di Torino（都灵理工大学）；Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；National Institute for Health Research（英国国家卫生研究院）；University College London Hospitals（伦敦大学学院医院）
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）
Anatomically Conditioned Recurrent Refinement for Topology-Aware Circle of Willis Segmentation
Faculty of Electrical Engineering, Computer Science and Information Technology, Osijek, Croatia（奥西耶克大学电气工程、计算机科学与信息技术学院）；Clinical Medical Center Osijek, Osijek, Croatia（奥西耶克临床医学中心）；Ghent University, Dept. of Telecommunications and Information Processing, imec-TELIN-IPI, Ghent, Belgium（根特大学电信与信息处理系，imec-TELIN-IPI）；Ghent University, Dept. of Telecommunications and Information Processing, TELIN-GAIM, Ghent, Belgium（根特大学电信与信息处理系，TELIN-GAIM）
DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Erasmus MC（伊拉斯姆斯医学中心）；University of Copenhagen（哥本哈根大学）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland；Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW), Waedenswil, Switzerland；Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland；Department of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China；Department of Radiology at Weill Cornell Medicine, Cornell University, New York, USA；Institute for Tissue Engineering；School of Computation, Information；Technology, Technical University of Munich, Germany；Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, USA；School of Medicine；Health, TUM Klinikum, Technical University of Munich, Germany；Munich Center for Machine Learning, Munich, Germany；Department of Computing, Imperial College London, London, UK；Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands；Department of Neurology；Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands；Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands；Electronic \& Information Engineering School, Harbin Institute of Technology (Shenzhen), China；Peng Cheng Laboratory, Shenzhen, China；Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany；Faculty of Mathematics；Computer Science, Heidelberg University, Germany；Helmholtz Imaging, German Cancer Research Center, Heidelberg, Germany；Data Science School for Health, Karlsruhe/Heidelberg, Germany；Learning Group, Department of Radiation Oncology, Heidelberg University Hospital；Department of Radiology, University of Washington, Seattle, WA, USA；Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China；Department of Clinical Neurosciences, Division of Neurosurgery, Geneva University Hospitals, Geneva, Switzerland；Department of Neurology, University Hospital of Zurich, Zurich, Switzerland；Department of Physiology, University of Toronto, Canada；Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland；Department of Diagnostic Imaging, National University Hospital, Singapore；University of Chicago, USA；Department of Diagnostic；Interventional Neuroradiology, University Hospital Berne；University of Berne, Berne, Switzerland；Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montréal, Québec, Canada；DEEPNOID Inc., Seoul, South Korea；Department of Artificial Intelligence, Korea University, Seoul, South Korea；Charité Lab for AI in Medicine (CLAIM), Charité Universitätsmedizin Berlin, Berlin, Germany；Lung Institute, Faculty of Medicine, Imperial College London, London, UK；Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK；Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA；Institute of Medical Technology, Peking University Health Science Center, Beijing, China；Hangzhou Genlight MedTech Co., Ltd., China；Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China；Department of Automation, Shanghai Jiao Tong University, Shanghai, China；Department of Artificial Intelligence, Sungkyunkwan University, Seoul, South Korea；Department of Electrical；Computer Engineering, Sungkyunkwan University, Seoul, South Korea；Shanghai MediWorks Precision Instruments Co., Ltd., China；Institute of Informatics, HES-SO Valais-Wallis, Switzerland；Department of Measurement；Electronics, AGH University of Krakow, Poland；Laboratoire de Thermique et Energie de Nantes (LTeN), Université Nantes, Polytech’Nantes, Nantes, France；Research Institute of Computer Vision；Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA；Physense, BCN-Medtech, Department of Communication；Information Technologies, Universitat Pompeu Fabra, Barcelona, Spain；Department of Mathematical Modeling；Machine Learning, University of Zurich, Zurich, Switzerland；Laboratory of Brain Atlas；Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China；School of Computer；Information Engineering, Xiamen University of Technology, Xiamen, China；Vascular Research Center, University at Buffalo, NY, USA；Department of Pathology；Anatomical Sciences, University at Buffalo, NY, USA；Department of Neurosurgery, University at Buffalo, NY, USA；LPIXEL Inc., Tokyo, Japan
