Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ledig:2018:10.1038/s41598-018-29295-9,
author = {Ledig, C and Schuh, A and Guerrero and Heckemann, RA and Rueckert, D},
doi = {10.1038/s41598-018-29295-9},
journal = {Scientific Reports},
title = {Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database},
url = {http://dx.doi.org/10.1038/s41598-018-29295-9},
volume = {8},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.
AU  - Ledig,C
AU  - Schuh,A
AU  - Guerrero
AU  - Heckemann,RA
AU  - Rueckert,D
DO  - 10.1038/s41598-018-29295-9
PY  - 2018///
SN  - 2045-2322
TI  - Structural brain imaging in Alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-018-29295-9
UR  - http://hdl.handle.net/10044/1/62172
VL  - 8
ER  -
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