TY - GEN
T1 - Automatic cortical surface parcellation based on fiber density information
AU - Zhang, Degang
AU - Guo, Lei
AU - Li, Gang
AU - Nie, Jingxin
AU - Deng, Fan
AU - Li, Kaiming
AU - Hu, Xintao
AU - Zhang, Tuo
AU - Jiang, Xi
AU - Zhu, Dajiang
AU - Zhao, Qun
AU - Liu, Tianming
PY - 2010
Y1 - 2010
N2 - It is widely believed that the structural connectivity of a brain region largely determines its function. High resolution Diffusion Tensor Imaging (DTI) is now able to image the axonal fibers in vivo and the DTI tractography result provides rich connectivity information. In this paper, a novel method is proposed to employ fiber density information for automatic cortical parcellation based on the premise that fibers connecting to the same cortical region should be within the same functional brain network and their aggregation on the cortex can define a functionally coherent region. This method consists of three steps. Firstly, the fiber density is calculated on the cortical surface. Secondly, a flow field is obtained by calculating the fiber density gradient and a flow field tracking method is utilized for cortical parcellation. Finally, an atlas-based warping method is used to label the parcellated regions. Our method was applied to parcellate and label the cortical surfaces of eight healthy brain DTI images, and interesting results are obtained. In addition, the labeled regions are used as ROIs to construct structural networks for different brains, and the graph properties of these networks are measured.
AB - It is widely believed that the structural connectivity of a brain region largely determines its function. High resolution Diffusion Tensor Imaging (DTI) is now able to image the axonal fibers in vivo and the DTI tractography result provides rich connectivity information. In this paper, a novel method is proposed to employ fiber density information for automatic cortical parcellation based on the premise that fibers connecting to the same cortical region should be within the same functional brain network and their aggregation on the cortex can define a functionally coherent region. This method consists of three steps. Firstly, the fiber density is calculated on the cortical surface. Secondly, a flow field is obtained by calculating the fiber density gradient and a flow field tracking method is utilized for cortical parcellation. Finally, an atlas-based warping method is used to label the parcellated regions. Our method was applied to parcellate and label the cortical surfaces of eight healthy brain DTI images, and interesting results are obtained. In addition, the labeled regions are used as ROIs to construct structural networks for different brains, and the graph properties of these networks are measured.
KW - Cortical surface parcellation
KW - Fiber density
KW - Structure network
UR - http://www.scopus.com/inward/record.url?scp=77955205811&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2010.5490193
DO - 10.1109/ISBI.2010.5490193
M3 - 会议稿件
AN - SCOPUS:77955205811
SN - 9781424441266
T3 - 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings
SP - 1133
EP - 1136
BT - 2010 7th IEEE International Symposium on Biomedical Imaging
T2 - 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
Y2 - 14 April 2010 through 17 April 2010
ER -