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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

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

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Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author
Russian Science Foundation More from this funder
Publisher:
Springer Publisher's website
Journal:
8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017) Journal website
Host title:
8th International Workshop on Machine Learning in Medical Imaging (MLMI 2017)
Publication date:
2017-09-01
Acceptance date:
2017-07-16
DOI:
Source identifiers:
736896
Keywords:
Pubs id:
pubs:736896
UUID:
uuid:3ca5495c-bcba-401f-8c62-948eeda3f2ea
Local pid:
pubs:736896
Deposit date:
2017-10-17

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