Learning-based methods for infant brain images analysis
摘要: 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.
王利，美国北卡罗来纳大学教堂山分校，Department of Radiology and Biomedical Research Imaging Center，助理教授。
Dr. Li Wang is an Assistant Professor in the University of North Carolina at Chapel Hill. He completed his PhD in June 2010 from Nanjing University of Science and Technology. Li Wang is working in the University of North Carolina at Chapel Hill, USA. 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 identifying imaging-based biomarkers for early autism diagnosis, as well as designing imaging-based surgical planning for CMF deformities.