行知论坛195:Learning-based methods for infant brain images analysis

时间:2018-11-15浏览:10设置

行知论坛第195期 (海外学术报告)


报告时间:20181121日(周三)下午 2:304:30

报告地点:计算机学院4001


报告题目:Learning-based methods for infant brain images analysis

报 告 人:Dr Li Wang, Assistant Professor, University of North Carolina at Chapel Hill

邀 请 人:孙权森 教授


报告简介:Recent progress in infant MRI technology allows us to track the dynamic brain developmental trajectories in vivo during the first year of life, which can greatly increase our very limited knowledge on normal early brain development, and also provide important insights into early neurodevelopmental disorders, such as autism spectrum disorder and schizophrenia. However, the existing neuroimaging computational tools, which were mainly developed for older children and adult brains, are ill-suited for infant brain studies, due to great challenges in tissue segmentation and labeling, caused by the extremely low contrast, insufficient resolution, severe partial volume effects, and dynamic growth. In this presentation, Dr. Wang will introduce learning-based methods for infant brain images analysis, including tissue segmentation of cerebrum and cerebellum, hippocampal subfield, and imaging-biomarkers for early diagnosis of autism.


报告人简介:王利,男,博士,助理教授,任职于北卡罗来纳大学教堂山分校,IEEE Senior member20106月毕业于南京理工大学计算机科学与技术学院,获博士学位;20107月至今20157月,在北卡罗来纳大学教堂山分校做博士后;20157月至20167月在北卡罗来纳大学教堂山分校任职讲师;20167月至今任职助理教授。多年来一直致力于研究婴幼儿大脑研究,包括分割,重建,早期诊断,获得NIH Career Award (K01)NIH R01项目;在国内外学术刊物和国际会议上发表学术论文138余篇,其中被SCI检索60余篇。Google Scholar总引用3447次,h-index=30





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