Developing AI Algorithms for Generating and Segmenting Multimodal MRI Brain Images Across Species

On Demand Session

Webinar Overview

Accurately segmenting brain structures in magnetic resonance imaging (MRI) data is essential for subsequent analysis. However, there is currently a lack of generalizable methods for extracting brain tissues from multimodal MRI data across species, including rodents, nonhuman primates, and humans. Therefore, the development of a flexible and generalizable method would enable researchers to analyze and compare experimental results more effectively. Here, we introduce deep learning-based networks for generating and segmenting multimodal MRI brain images across species, modalities, and MRI scanners.

On Demand Session

Speaker

Xiao-Yong Zhang

Shanghai Jiao Tong University School of Medicine

Dr. Xiao-Yong Zhang is a professor at the Shanghai Jiao Tong University School of Medicine. His research focuses primarily on magnetic resonance imaging (MRI), artificial intelligence analysis, and molecular imaging. He is particularly interested in visualizing the brain microenvironment and developing machine learning algorithms. Dr. Zhang has authored over 50 academic papers, including publications in Medical Image Analysis, IEEE Transactions on Medical Imaging, and others. Additionally, Dr. Zhang has led several research projects funded by the National Natural Science Foundation of China as a principal investigator.

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