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【医学部学术讲座】——Computational Anatomy: Quantitative Study of Neuroanatomy with Diffeomorphic Coordinate Transformation

文章来源: 作者: 发布时间:2017年04月25日 点击数: 字体:

主讲嘉宾:唐晓颖 中山大学  助理教授

时间:     2017年4月26日下午 14:00 – 15:30

地点:     深圳大学医学院725会议室

主持人:   倪东 副教授

单位:医学超声关键技术国家地方联合工程实验室、广东省生物信息检测与医学超声成像重点实验

报告内容简介: 

Computational Anatomy (CA) is an emerging discipline that aims to understand neuroanatomy by utilizing a comprehensive set of mathematical tools from Riemannian geometry alongside a “textbook” composed of MR images from a variety of modalities. Central to CA is the formulation of correspondences between different coordinate systems. In this talk, I will introduce the basics of CA before presenting a sophisticated algorithm for the creation of such spatial correspondences between anatomical systems. With this established, I will cover two main practical applications of CA in the quantitative study of neuroanatomy. The first one concerns how we can create a hierarchical structure identification system for the human brain and I will introduce an original pipeline designed for brain parcellation using T1-weighted images and diffusion tensor images. The second application will focus on parcellation and shape based neuroinformatics of the brain's anatomy along with their relevance to the diagnosis and prognosis of various neurodegenerative disorders. Results from numerous clinical datasets will be presented.

 

报告嘉宾简介:

Xiaoying Tang, Ph.D., Assistant Professor at the SYSU-CMU Joint Institute of Engineering, SYSU, is also jointly appointed as an Associate Professor at the School of Electronics and Information Technology, SYSU and holds the Adjunct Professorship at the Electrical and Computer Engineering (ECE) Department of Carnegie Mellon University and the ECE Department of Johns Hopkins University. Dr. Tang obtained her Ph.D. degree from Johns Hopkins University in 2014 and her research focuses upon mathematical medical image analysis, brain segmentation and registration, multi-modality MRI analysis, statistical shape analysis, machine learning, and neuroinformatics. Dr. Tang is an associate editor of the Journal of Alzheimer’s Disease as well as the guest associate editor of Frontier in Neuroscience and Frontiers in Neurology.

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