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Machine learning for large-scale quality control of 3D shape models in neuroimaging

Abstract:

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 humanrated subjects, training kernel...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Publisher copy:
10.1007/978-3-319-67389-9_43

Authors


Gutman, BA More by this author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Psychiatry
Russian Science Foundation More from this funder
Publisher:
Springer Publisher's website
Publication date:
2017-09-24
Acceptance date:
2017-07-16
DOI:
Pubs id:
pubs:736896
URN:
uri:3ca5495c-bcba-401f-8c62-948eeda3f2ea
UUID:
uuid:3ca5495c-bcba-401f-8c62-948eeda3f2ea
Local pid:
pubs:736896

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