报告题目：Learning-based method for medical image segmentation (基于学习方法的医学图像分割)
报告摘要： Segmentation of medical images is challenging due to the low image quality, severe partial volume effect, and noise. In this talk, I will mainly focus on the segmentation of infant brain MRIs, monkey MRIs and dental CBCTs. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6-8 months of age, where the white and gray matter tissues are isointense and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this talk, a novel learning-based method is proposed for segmentation of infant brain MRIs. This method is general and can be also extended to monkey MRIs and dental CBCTs.
个人简介: Li Wang received the Ph.D. degree in Pattern Recognition and Intelligent System from Nanjing University of Science and Technology in 2010. He is currently a research instructor from the University of North Carolina at Chapel Hill, USA. His interests focus on segmentation, registration, cortical surface analysis, and their applications on normal early brain development and disorders.