Deep learning approaches may help radiologists in the early diagnosis and timely treatment of cerebrovascular diseases. Accurate cerebral vessel segmentation of Time-of-Flight Magnetic Resonance Angiographs (TOF-MRAs) is an essential step in this process. This study investigates deep learning approaches for automatic, fast and accurate cerebrovascular segmentation for TOF-MRAs. The performance of several data augmentation and selection methods for training a 2D and 3D U-Net for vessel segmentation was investigated in five experiments: a) without augmentation, b) Gaussian blur, c) rotation and flipping, d) Gaussian blur, rotation and flipping and e) different input patch sizes. All experiments were performed by patch-training both a 2D and 3D U-Net and predicted on a test set of MRAs. Ground truth was manually defined using an interactive threshold and region growing method. The performance was evaluated using the Dice Similarity Coefficient (DSC), Modified Hausdorff Distance and Volumetric Similarity, between the predicted images and the interactively defined ground truth. The segmentation performance of all trained networks on the test set was found to be good, with DSC scores ranging from 0.72 to 0.83. Both the 2D and 3D U-Net had the best segmentation performance with Gaussian blur, rotation and flipping compared to other experiments without augmentation or only one of those augmentation techniques. Additionally, training on larger patches or slices gave optimal segmentation results. In conclusion, vessel segmentation can be optimally performed on TOF-MRAs using a trained 3D U-Net on larger patches, where data augmentation including Gaussian blur, rotation and flipping was performed on the training data.
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）
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Department of Radiology, University Medical Center Utrecht（乌得勒支大学医学中心放射科）；Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht（乌得勒支大学医学中心神经病学与神经外科系，乌得勒支大学医学中心脑中心）
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）
Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networks
Charité Universitätsmedizin Berlin（柏林夏里特医学院）；Technical University Berlin（柏林工业大学）；Birmingham City University（伯明翰城市大学）；Fraunhofer MEVIS（弗劳恩霍夫医学图像计算研究所）；Beuth University of Applied Sciences（拜伊特应用科学大学）
数据的匿名化和共享是隐私保护和医学图像分析大数据集获取的关键。这是一个很大的挑战，尤其是对神经成像。在这里，大脑的独特结构允许重新识别，因此需要非传统的匿名化。生成对抗网络（GANs）有可能在保持预测特性的同时提供匿名图像。分析脑血管分割，我们在飞行时间（TOF）磁共振血管造影（MRA）贴片上训练了3个GANs，用于图像标签生成：1）深度卷积GAN，2）具有梯度惩罚的Wasserstein-GAN（WGAN-GP）和3）具有谱归一化的WGAN-GP（WGAN-GP-SN）。从每个GAN生成的图像标签被用于训练用于分割的U-网络，并在真实数据上进行测试。此外，我们使用迁移学习在第二个数据集上应用了我们的合成补丁。对于越来越多的多达15名患者，我们评估了有和没有预训练的真实数据的模型性能。通过Dice相似系数（DSC）和Hausdorff距离的第95百分位数（95 HD）评估所有模型的性能。比较3个GANs，在WGAN-GP-SN生成的合成数据上训练的U-网络显示出最高的预测血管的性能（DSC/95 HD 0.82/28.97），这是由在真实数据上训练的U-网络（0.89/26.61）进行基准测试的。对于相同的GAN，迁移学习方法显示出优于无预训练的性能，特别是对于仅一名患者（0.91/25.68 vs. 0.85/27.36）。在这项工作中，合成图像标签对保留了可推广的信息，并表现出…
Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Seoul National University College of Medicine, Seoul National University Bundang Hospital（首尔大学医学院，首尔大学盆唐医院）
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）
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）
SPOCKMIP: Segmentation of Vessels in MRAs with Enhanced Continuity using Maximum Intensity Projection as Loss
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；German Centre for Neurodegenerative Disease（德国神经退行性疾病中心）；Centre for Behavioural Brain Sciences（行为脑科学中心）；Human Technopole（人类技术园）；Genomics Research Centre（基因组学研究中心）
Learned Local Attention Maps for Synthesising Vessel Segmentations
University of Leeds（利兹大学）；Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心（CISTIB））；NIHR Leeds Biomedical Research Centre (BRC)（利兹国家健康研究院生物医学研究中心（BRC））；Alan Turing Institute（艾伦图灵研究所）；KU Leuven（鲁汶大学）；Medical Imaging Research Center (MIRC)（医学成像研究中心（MIRC））
SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms
Faculty of Computer Science, Otto von Guericke University Magdeburg（奥托·冯·格里克大学马格德堡分校计算机科学学院）；Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg（奥托·冯·格里克大学马格德堡分校数据与知识工程小组）；Human Technopole（人类技术极地）；Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg（生物医学磁共振，奥托·冯·格里克大学马格德堡分校）；Department of Neurology, Medical Faculty, University Hospital of Magdeburg（马格德堡大学医院医学系神经科）；German Centre for Neurodegenerative Diseases（德国神经退行性疾病研究中心）；Centre for Behavioural Brain Sciences, Magdeburg（行为脑科学中心，马格德堡）；Department of Neurology, University Hospital Zurich（苏黎世大学医院神经科）；Department of Consultation-Liaison-Psychiatry and Psychosomatic Medicine, University Hospital Zurich（苏黎世大学医院咨询-联络精神病学与心身医学科）；Stanford University（斯坦福大学）；Translational Radiobiology, Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（转化放射生物学，放射肿瘤学部，埃尔兰根大学医院，埃尔兰根-纽伦堡弗里德里希-亚历山大大学）；National Yang Ming Chiao Tung University（阳明交通大学）；School of Electrical Engineering and Computer Science, University of Queensland（昆士兰大学电气工程与计算机科学学院）；Australian eHealth Research Centre, CSIRO（澳大利亚eHealth研究中心，CSIRO）；National Heart and Lung Institute, Faculty of Medicine, Imperial College London（英国伦敦帝国理工学院医学系国家心脏和肺研究所）；Hawkes Institute, Department of Computer Science, University College London（霍克斯研究所，伦敦大学学院计算机科学系）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates（阿布扎克穆罕默德·本·扎耶德人工智能大学）；The Alan Turing Institute, London, UK（艾伦·图灵研究所，伦敦，英国）；School of Computing, University of Leeds（利兹大学计算学院）；Department of Computer Science at School of Informatics, Xiamen University（厦门大学信息学院计算机科学系）；Manteia Technologies Co., Ltd, Xiamen, China（厦门Manteia技术有限公司）；Leicester International Institute, Dalian University of Technology（大连理工大学利兹国际学院）；Sorbonne Université, Institut du Cerveau - Paris Brain Institute（索邦大学，巴黎脑研究所）；Centre of Formation and Research in Artificial Intelligence, Universidad de Los Andes, Colombia（智利洛斯安德斯大学人工智能培训与研究中心）；Data Science Department, EURECOM, Sophia Antipolis, France（EURECOM数据科学系，法国索菲亚安蒂波利斯）
