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High-resolution Crvo-EM Studies of Amyloid Fibrils in Neurodegenerative Diseases
Recent Advances of Phosphorescent Metal Complexes
环境学术沙龙第702期:城市大气新粒子生成与生长
最优潮流的可行性恢复映射深度神经网络
报告题目:
Neuroimage-based Diagnosis of Brain Disorders
 报告人:
Dinggang Shen
University of North Carolina at Chapel Hill
报告时间:
2014-04-28 14:00
报告地点:
医学科学楼B323
主办单位:
医学院
  简介:
报告摘要:
This talk will summarize our work on analysis of MR brain images. Our main goal is to develop automated analysis methods for precise quantification of subtle and complex structural/functional changes in the brains, with applications in early detection of brain disorders, such as Alzheimer's Disease (AD). To achieve this goal, we developed a 3D brain registration method, called HAMMER, for inter-subject registration, and also a 4D (3 spatial dimensions + 1 temporal dimension) brain registration method to obtain more accurate measurements for tiny longitudinal brain changes, compared to the 3D registration methods. For better alignment of a population of images, we have recently developed several groupwise registration methods for joint registration of all images together, thus further improving the registration accuracy among all images. With accurate brain structural/functional information measured by our registration methods, we further developed a multivariate analysis method, based on support vector machine, to jointly consider all structural/functional changes for determining the group difference between normal and abnormal brains, i.e., due to diseases, aging, or brain disorders. We have applied our developed methods to computer-aided diagnosis of schizophrenia and AD. Details of these 3D, 4D, and groupwise registration methods, as well as brain classification methods, will be discussed in this talk.
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