Accurate segmentation of MR brain tissue is a crucial step for diagnosis,surgical planning, and treatment of brain abnormalities. However,it is a time-consuming task to be performed by medical experts. So, automatic and reliable segmentation methods are required. How to choose appropriate training dataset from limited labeled dataset rather than the whole also has great significance in saving training time. In addition, medical data labeled is too rare and expensive to obtain extensively, so choosing appropriate unlabeled dataset instead of all the datasets to annotate, which can attain at least same performance, is also very meaningful. To solve the problem above, we design an automatic segmentation method based on U-shaped deep convolutional network and obtain excellent result with average DSC metric of 0.8610, 0.9131, 0.9003 for Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM) respectively on the well-known IBSR18 dataset. We use bootstrapping algorithm for selecting the most effective training data and get more state-of-the-art segmentation performance by using only 50% of training data. Moreover, we propose a strategy of MR brain tissue images' suggestive annotation for unlabeled medical data based on the modified U-net. The proposed method performs fast and can be used in clinical.
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
Ecole de Technologie Superieure (ETS), University of Quebec（魁北克大学高等技术学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Xidian University（西安电子科技大学）；Korea University（高丽大学）
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；Technical University of Munich（慕尼黑工业大学）；Brain Research Institute, University of Zurich（苏黎世大学脑研究所）；University of Debrecen（德布勒森大学）
DuMeta++: Spatiotemporal Dual Meta-Learning for Generalizable Few-Shot Brain Tissue Segmentation Across Diverse Ages
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）；Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University（教育部生物医学信息工程重点实验室，西安交通大学生命科学与技术学院）
mViSE: A Visual Search Engine for Analyzing Multiplex IHC Brain Tissue Images
Cullen College of Engineering, University of Houston（休斯顿大学塞勒恩工程学院）；The University of Texas McGovern Medical School（德克萨斯大学麦格芬医学学院）；National Institute of Neurological Disorders and Stroke（国家神经疾病与中风研究所）
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
University Hospital Basel（巴塞尔大学医院）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Helmholtz AI（亥姆霍兹人工智能）；Technical University of Munich（慕尼黑工业大学）；University of Basel（巴塞尔大学）；University of Zurich（苏黎世大学）；Klinikum rechts der Isar（伊斯河畔医院）；TranslaTUM - Central Institute for Translational Cancer Research（慕尼黑工业大学转化肿瘤学中央研究所（TranslaTUM））
Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Department of Computer Science, University of North Carolina at Chapel Hill, USA（北卡罗来纳大学教堂山分校计算机科学系）；Department of Computer Science, University of Salzburg, Austria（萨尔茨堡大学计算机科学系）；Kitware Inc., USA（Kitware公司）；Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, USA（宾夕法尼亚大学佩雷尔曼医学院生物医学图像计算与分析中心）；Department of Computer Science, Technical University of Munich, Germany（慕尼黑工业大学计算机科学系）；Institute of Neuroimaging and Informatics, University of Southern California, USA（南加州大学神经影像与信息学研究所）；David Geffen School of Medicine, UCLA Medical Center, USA（加州大学洛杉矶分校医学中心大卫·格芬医学院）
Improving Across-Dataset Brain Tissue Segmentation Using Transformer
Columbia University（哥伦比亚大学）；Mortimer B. Zuckerman Mind Brain Behavior Institute（Mortimer B. Zuckerman 心智与脑行为研究所）
脑组织分割在通过基于体素的形态学量化MRI数据和突出与脑内各种状况相关的细微结构变化方面具有很大的实用性。然而，手动分割是高度劳动密集型的，并且由于MRI采集的固有属性，自动化方法一直在努力，因此非常需要有效的分割工具。尽管最近深度卷积神经网络（CNN）在脑组织分割方面取得了成功，但许多此类解决方案并不能很好地推广到新的数据集，这对于可靠的解决方案至关重要。Transformers已经在自然图像分割中取得了成功，并且最近已被应用于3D医学图像分割任务，因为它们能够在CNN的局部感受野挣扎的输入中捕获长距离关系。这项研究介绍了一种新的CNN-Transformer混合架构，设计用于脑组织分割。我们在四个多站点T1 w MRI数据集上验证了我们模型的性能，这些数据集涵盖了不同的供应商、场强、扫描参数、时间点和神经精神疾病。在所有情况下，我们的模型实现了最大的通用性和可靠性。Out方法具有固有的鲁棒性，可以作为脑相关T1 w MRI研究的有价值的工具。TABS网络的代码可在https://github.com/raovish6/TABS上获得。
Brain Tissues Segmentation on MR Perfusion Images Using CUSUM Filter for Boundary Pixels
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“伊戈尔·西科尔斯基基辅理工学院”）；Taras Shevchenko National University of Kyiv（基辅塔拉斯·舍甫琴科国立大学）
End-to-end learning of brain tissue segmentation from imperfect labeling
The Mind Research Network（心智研究网络）；University of New Mexico（新墨西哥大学）；New College of Florida（新佛罗里达学院）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）
Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentation
Lausanne University Hospital (CHUV)（洛桑大学医院（CHUV））；University of Lausanne (UNIL)（洛桑大学（UNIL））；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；University Children’s Hospital Zurich（苏黎世大学儿童医院）；University of Zurich（苏黎世大学）；Neuroscience Center Zurich（苏黎世神经科学中心）
Advanced Brain Tissue Imaging with Data-Consistent Diffusion Priors in Laminographic X-Ray Nanoimaging
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Paul Scherrer Institute（保罗·谢勒研究所）；The Francis Crick Institute（弗朗西斯·克里克研究所）；Mineral Resource CSIRO（澳大利亚联邦科学与工业研究组织矿产资源部）；ESRF, The European Synchrotron（欧洲同步辐射光源）
Department of Electrical Engineering, Indian Institute of Technology Madras (IITM)（电子工程系，印度理工学院Madras（IITM））；Sudha Gopalakrishnan Brain Centre (SGBC), IITM（Sudha Gopalakrishnan脑中心（SGBC），IITM）
StyleSeg V2: Towards Robust One-shot Segmentation of Brain Tissue via Optimization-free Registration Error Perception
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology（华中科技大学武汉光电国家研究中心）；Wuhan United Imaging Healthcare Surgical Technology Co., Ltd.（武汉联影外科技术有限公司）
Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets
King’s College London（伦敦国王学院）；School of Biomedical Engineering & Imaging Sciences（生物医学工程与成像科学学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民健康服务基金会信托）；NVIDIA（英伟达）；Dementia Research Centre, UCL Institute of Neurology, UCL（伦敦大学学院痴呆研究中心、伦敦大学学院神经学研究所、伦敦大学学院）；Department of Medical Physics, UCL（伦敦大学学院医学物理系）；Department of Neuroradiology（神经放射科）；St. Thomas’ Hospital（圣托马斯医院）
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes
ShanghaiTech University（上海科技大学）；Fudan University（复旦大学）；Obstetrics and Gynecology Hospital of Fudan University（复旦大学附属妇产科医院）；Northwestern Polytechnical University（西北工业大学）；Shanghai United Imaging Intelligence company Ltd.（上海联影智能科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究中心）
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation
Department of Neuro-Oncology, Massachusetts General Hospital（麻省总医院神经肿瘤科）；MGH & BWH Center for Clinical Data Science（麻省总医院与布莱根妇女医院临床数据科学中心）；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）；Massachusetts General Hospital（麻省总医院）；NVIDIA（英伟达）
Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
University of Girona（赫罗纳大学）；University of Burgundy（勃艮第大学）；University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）
Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation
Computational Imaging Research Lab（计算成像研究实验室）；Department of Biomedical Imaging and Image-guided Therapy（生物医学成像与影像引导治疗部门）；Medical University of Vienna（维也纳医科大学）；Comprehensive Center for Artificial Intelligence in Medicine（医学人工智能综合中心）；Division of General Radiology（普通放射学分会）；Medical University Vienna（维也纳医科大学）；Christian Doppler Laboratory for Mathematical Modelling and Simulation of Next-Generation Medical Ultrasound Devices（基督教·多普勒医学影像建模与模拟实验室）
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
Lausanne University Hospital（洛桑大学医院）；University of Lausanne (UNIL)（洛桑大学）；CIBM Center for Biomedical Imaging（CIBM生物医学成像中心）；King’s College London（伦敦国王学院）；FAU Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）
Automated Segmentation of Coronal Brain Tissue Slabs for 3D Neuropathology
Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州总医院和哈佛医学院生物医学成像中心）；Massachusetts Alzheimer’s Disease Research Center, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州阿尔茨海默病研究中心，马萨诸塞州总医院和哈佛医学院）；Department of Neurological Surgery, University of Washington School of Medicine（华盛顿大学医学院神经外科部门）；Department of Laboratory Medicine and Pathology, University of Washington School of Medicine（华盛顿大学医学院实验室医学与病理学部门）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation
Ege University（埃格大学）；National Institute on Aging, National Institutes of Health（美国国立卫生研究院国家老龄化研究所）；Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院）；University of Central Florida（中佛罗里达大学）
Segmentation of Gray Matters and White Matters from Brain MRI data
Department of Computer Science and Engineering（计算机科学与工程系）；Waseda University（早稻田大学）；Department of Neurosurgery（神经外科）；Teraoka Memorial Hospital（寺田纪念医院）；Department of Radiology（放射科）；Nippon Medical School Hospital（日本医学大学医院）；Tokyo Rosai Hospital（东京罗赛医院）
An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images
School of Computer Science, Queensland University of Technology（昆士兰科技大学计算机科学学院）；School of Electrical Engineering and Robotics, Queensland University of Technology（昆士兰科技大学电气工程与机器人学院）；Centre for Data Science, Queensland University of Technology（昆士兰科技大学数据科学中心）
