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

Johnson, EB; Scahill, RI; Tabrizi, SJ; (2019) Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images. Journal of Visualized Experiments , 143 , Article e58198. 10.3791/58198. Green open access

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Abstract

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.

Type: Article
Title: Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.3791/58198
Publisher version: https://doi.org/10.3791/58198
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases
URI: https://discovery.ucl.ac.uk/id/eprint/10067123
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