报告题目：Learning-based methods for infant brain images analysis
报告时间：2018年11月20日下午3:00
报告地点：尚贤楼706
主 持 人：陈允杰 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.
专家简介：
Li Wang is an Assistant Professor of Department of Radiology and Biomedical Research Imaging Center at the University of North Carolina at Chapel Hill, USA. He received the Ph.D. degree from the School of Computer Science and Engineering, Nanjing University of Science and Technology, China, in 2010. He has published over 60 journal and conference papers with some best paper award papers. His research interests focus on image segmentation, image registration, cortical surface analysis, machine learning and their applications to normal early brain development and disorders. His currently focus is on the neonatal brain image segmentation and serial infant brain images segmentation.
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数学与统计学院
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