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½²×ù: Image Computing in Baby Brain Mapping

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Àî¸Õ²©Ê¿£¬ÃÀ¹ú±±¿¨ÂÞÀ´ÄÉ´óѧ½ÌÌÃɽ·ÖУ(University of North Carolina at Chapel Hill)·ÅÉäϵºÍÉúÎïҽѧ¹¤³ÌϵÖúÀí½ÌÊÚ¡£ÖØÒª×êÑз½ÏòΪ¿ª·¢Éñ¾­Ó°ÏñÖÇÄÜÍÆËãºÍ·ÖÎö²½Ö裬ÓÃÓÚ×êÑÐÓ¤Ó׶ùºÍÌ¥¶ù½×¶ÎÄԽṹºÍÖ°Äܵķ¢ÓýºÍÓйصÄÄÔ·¢Óý¼²²¡¡£ÔÚ¹ú¼Ê³ÛÃûѧÊõÆÚ¿¯ºÍ»áÒé°ä·¢ÂÛÎÄ150¶àƪ£¬Ô̺¬Cell, PNAS, Cerebral Cortex, Journal of Neuroscience, NeuroImage, Brain Structure and Function, Human Brain Mapping, Medical Image Analysis, IEEE Trans. on Medical Imaging, IPMI, MICCAIµÈ¡£ÂÛÎı»ÒýÓÃ3000ÂŴΣ¬¹È¸èѧÊõH-indexΪ33¡£Ö÷³Ö¶àÏîNIH¿ÆÑÐÏîÄ¿, »ñµÃNIH Career Award¡£

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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.

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