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Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis

Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS...

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Main Authors: Brugnara, Gianluca (Author) , Isensee, Fabian (Author) , Neuberger, Ulf (Author) , Bonekamp, David (Author) , Petersen, Jens (Author) , Diem, Ricarda (Author) , Wildemann, Brigitte (Author) , Heiland, Sabine (Author) , Wick, Wolfgang (Author) , Bendszus, Martin (Author) , Maier-Hein, Klaus H. (Author) , Vollmuth, Philipp (Author)
Format: Article (Journal)
Language:English
Published: 3 January 2020
In: European radiology
Year: 2020, Volume: 30, Issue: 4, Pages: 2356-2364
ISSN:1432-1084
DOI:10.1007/s00330-019-06593-y
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00330-019-06593-y
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Author Notes:Gianluca Brugnara, Fabian Isensee, Ulf Neuberger, David Bonekamp, Jens Petersen, Ricarda Diem, Brigitte Wildemann, Sabine Heiland, Wolfgang Wick, Martin Bendszus, Klaus Maier-Hein, Philipp Kickingereder
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Summary:Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI.
Item Description:Gesehen am 14.04.2020
Physical Description:Online Resource
ISSN:1432-1084
DOI:10.1007/s00330-019-06593-y

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