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Automated lesion segmentation with BIANCA: Impact of population-level features, classification algorithm and locally adaptive thresholding

Abstract:

White matter hyperintensities (WMH) or white matter lesions exhibit high variability in their characteristics both at population- and subject-level, making their detection a challenging task. Population-level factors such as age, vascular risk factors and neurodegenerative diseases affect lesion load and spatial distribution. At the individual level, WMH vary in contrast, amount and distribution in different white matter regions. In this work, we aimed to improve BIANCA, the FSL tool for WMH...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.neuroimage.2019.116056

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Oxford college:
Somerville College
Role:
Author
ORCID:
0000-0002-9451-4779
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Clinical Neurosciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Clinical Neurosciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Clinical Neurosciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Clinical Neurosciences
Role:
Author
More from this funder
Name:
Wellcome Trust
Funding agency for:
Husain, M
Grant:
206330/Z/17/Z
More from this funder
Name:
National Institute for Health Research
Funding agency for:
Zamboni, G
Rothwell, P
Husain, M
Jenkinson, M
Griffanti, L
Grant:
206330/Z/17/Z
More from this funder
Name:
Medical Research Council
Funding agency for:
Griffanti, L
More from this funder
Name:
Parkinsons UK
Funding agency for:
Griffanti, L
Publisher:
Elsevier
Journal:
NeuroImage More from this journal
Volume:
202
Article number:
116056
Publication date:
2019-07-31
Acceptance date:
2019-07-24
DOI:
EISSN:
1095-9572
ISSN:
1053-8119
Keywords:
Pubs id:
pubs:1036611
UUID:
uuid:0b473787-4699-4644-8337-fb1dd96e249b
Local pid:
pubs:1036611
Source identifiers:
1036611
Deposit date:
2019-07-31

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