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dc.contributor.author
Wegmayr, Viktor
dc.contributor.author
Buhmann, Joachim M.
dc.date.accessioned
2021-03-22T13:54:07Z
dc.date.available
2020-11-19T03:47:39Z
dc.date.available
2020-11-19T09:56:20Z
dc.date.available
2021-03-22T13:54:07Z
dc.date.issued
2021-03
dc.identifier.issn
0920-5691
dc.identifier.issn
1573-1405
dc.identifier.other
10.1007/s11263-020-01384-1
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/451919
dc.identifier.doi
10.3929/ethz-b-000451919
dc.description.abstract
White matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is determined by an information-theoretic technique, the expected log-posterior agreement concept. It relies on the requirement that the posterior distributions of streamlines, inferred on retest measurements of the same subject, should yield stable results within the precision determined by the noise level of the data source.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Diffusion MRI
en_US
dc.subject
Brain
en_US
dc.subject
Tractography
en_US
dc.subject
Machine Learning
en_US
dc.subject
Maximum-entropy inference
en_US
dc.subject
Algorithm validation
en_US
dc.title
Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-11-06
ethz.journal.title
International Journal of Computer Vision
ethz.journal.volume
129
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
Int J Comput Vis
ethz.pages.start
656
en_US
ethz.pages.end
680
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Dordrecht
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00022 - Bereich VP Forschung / Domain VP Research::02803 - Collegium Helveticum / Collegium Helveticum
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
ethz.date.deposited
2020-11-19T03:47:46Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-22T13:54:16Z
ethz.rosetta.lastUpdated
2022-03-29T05:55:33Z
ethz.rosetta.versionExported
true
ethz.COinS
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