Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

被引:0
|
作者
Johnson, Eileanoir B. [1 ]
Scahill, Rachael I. [1 ]
Tabrizi, Sarah J. [1 ]
机构
[1] UCL Inst Neurol, Huntingtons Dis Res Ctr, London, England
来源
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS | 2019年 / 143期
关键词
Neuroscience; Issue; 143; MRI; structural; SPM; FSL; FreeSurfer; ANTs; MALP-EM; quality control; grey matter; SURFACE-BASED ANALYSIS; THICKNESS;
D O I
10.3791/58198
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.
引用
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页数:10
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