Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging
Northeastern University（东北大学）；Erasmus MC（伊拉斯姆斯医学中心）；University of Edinburgh（爱丁堡大学）
Bo Li, Marius de Groot, Rebecca M. E. Steketee, Rozanna Meijboom, Marion Smits, Meike W. Vernooij, M. Arfan Ikram, Jiren Liu, Wiro J. Niessen, Esther E. Bron
Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: error = 1% ~ 5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.
Learning Anatomical Segmentations for Tractography from Diffusion MRI
German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病研究中心）；A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院A. A. Martinos生物医学影像中心）；Department of Radiology, Harvard Medical School（哈佛医学院放射学系）
SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning
University at Buffalo, State University of New York（纽约州立大学布法罗分校）；New York University School of Medicine（纽约大学医学院）；Peking Union Medical College Hospital（北京协和医院）；Peking Union Medical College（北京协和医学院）；Chinese Academy of Medical Sciences（中国医学科学院）；SIAT, CAS（中国科学院深圳先进技术研究院）；Virginia Tech（弗吉尼亚理工大学）
atTRACTive: Semi-automatic white matter tract segmentation using active learning
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；German Cancer Consortium (DKTK)（德国癌症联盟）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）
An Arbitrary-Modal Fusion Network for Volumetric Cranial Nerves Tract Segmentation
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Department of Computer Science and Engineering, Department of Chemical and Biological Engineering and Center for Aging Science , Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Department of Radiology, Xiangya Hospital, Central South University（中南大学湘雅医院放射科）；Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province（陕西核工业215医院神经外科）；Department of Neurosurgery, Taihe Hospital of Wannan Medical College（皖南医学院太和医院神经外科）
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging
Weill Cornell Medicine（威尔康奈尔医学院）；Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院马丁诺斯生物医学成像中心）；Harvard Medical School（哈佛医学院）；Massachusetts Institute of Technology（麻省理工学院）；Cornell University（康奈尔大学）；Cornell Tech（康奈尔理工学院）
Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning
University of California, San Diego（加州大学圣地亚哥分校）；Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；VA San Diego Healthcare System（VA圣地亚哥医疗系统）
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation
Institute for Machine Learning, ETH Zurich（苏黎世联邦理工学院机器学习研究所）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Champalimaud Foundation（尚帕利莫基金会）
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors
IMT Atlantique（大西洋国立高等矿业电信学校）；LaTIM UMR 1101, Inserm（法国国家健康与医学研究院LaTIM UMR 1101实验室）；Centre Hospitalier Régional et Universitaire (CHRU) de Brest（布雷斯特大学区域中心医院）；Fondation ILDYS（ILDYS基金会）；Symbiosis Center for Medical Image Analysis, Symbiosis International University（共生国际大学共生医学图像分析中心）
Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging
Indian Institute of Technology Kharagpur（印度理工学院卡拉格布尔分校）
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Northwestern Polytechnical University（西北工业大学）；University of Pittsburgh（匹兹堡大学）；University of Sydney（悉尼大学）；JD Finance America Corporation（京东金融美国公司）
Semantic segmentation of multispectral photoacoustic images using deep learning
German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；Heidelberg University（海德堡大学）；HIDSS4Health - Helmholtz Information and Data Science School for Health（HIDSS4Health——亥姆霍兹健康信息与数据科学学院）；Cancer Research UK Cambridge Institute（英国癌症研究院剑桥研究所）；University of Cambridge（剑桥大学）
Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
Junia（朱尼亚学院）；Centrale Lille（里尔中央理工学院）；University of the Basque Country UPV/EHU（巴斯克大学 UPV/EHU）；Université Polytechnique Hauts-de-France（上法兰西理工大学）；Université de Lille（里尔大学）；Ho Chi Minh City Open University（胡志明市开放大学）
