講座: Image Computing in Baby Brain Mapping
李剛博士，美國北卡羅來納大學教堂山分校(University of North Carolina at Chapel Hill)放射系和生物醫學工程系助理教授。主要研究方向為開發神經影像智能計算和分析方法，用于研究嬰幼兒和胎兒階段腦結構和功能的發育和相關的腦發育疾病。在國際著名學術期刊和會議發表論文150多篇，包括Cell, PNAS, Cerebral Cortex, Journal of Neuroscience, NeuroImage, Brain Structure and Function, Human Brain Mapping, Medical Image Analysis, IEEE Trans. on Medical Imaging, IPMI, MICCAI等。論文被引用3000多次，谷歌學術H-index為33。主持多項NIH科研項目, 獲得NIH Career Award。
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast, large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this presentation, I will introduce our pioneered infant-dedicated computational tools for cortical surface-based analysis of early brain development. Several components in our tools capitalize on deep learning techniques. I will also show some neuroscience applications of our tools in revealing the dynamic, nonlinear and region-specific development of baby brains.