Thalamus segmentation based on resting-state functional magnetic resonance imaging
10.3760/cma.j.issn.1673-4181.2015.01.001
- VernacularTitle:一种基于静息态功能磁共振成像的快速丘脑分割算法
- Author:
Jing JIA
;
Qiang LI
;
Yu BAI
;
Liyi ZHANG
- Publication Type:Journal Article
- Keywords:
Resting-state functional magnetic resonance imaging;
k-means clustering algorithm;
Thalamus segmentation
- From:
International Journal of Biomedical Engineering
2015;38(1):1-4,后插3
- CountryChina
- Language:Chinese
-
Abstract:
Objective To obtain an accurate and effective method for thalamus segmentation based on resting-state functional magnetic resonance imaging (fMRI).Methods Based on the fact that resting-state fMRI technique examined spatial synchronization of spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals indirectly reflect the neuronal and synaptic activity,the in-thalamus BOLD signal correlations were calculated,and then the k-means clustering algorithm was applied to obtain functional connectivity-based thalamus segmentation.Results The thalamus was divided into seven regions.Voxels within the same region were highly correlated with each other.The segmentation result was similar to that divided by functional connectivity between thalamus and the cerebral cortex.Conclusions Resting-state fMRI could provide not only the functional connectivity network between cortical and subcortical brain regions,but also the functional characteristics of thalamus.Segmentation algorithm using only internal information of thalamus shows lower computational complexity and higher processing speed than that based on the functional connectivity between thalamus and the cerebral cortex.
