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

Journal: European radiology
Published Date: Jan 3, 2020

Abstract

OBJECTIVES: 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.

Authors

  • Gianluca Brugnara
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Fabian Isensee
  • Ulf Neuberger
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • David Bonekamp
    From the Department of Radiology (D.B., P.S., J.P.R., P.K., K.Y., M.F., H.P.S.), Division of Medical Image Computing (S.K., M.G., N.G., K.H.M.H.), Division of Statistics (M.W.), and Department of Medical Physics (T.A.K., F.D.), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany (D.B., H.P.S., K.H.M.H.); and Departments of Urology (J.P.R., B.H., M.H., B.A.H.) and Neuroradiology (P.K.), University of Heidelberg Medical Center, Heidelberg, Germany.
  • Jens Petersen
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Ricarda Diem
    Department of Neurology, University of Heidelberg Medical Center, Heidelberg, Germany.
  • Brigitte Wildemann
    Department of Neurology, University of Heidelberg Medical Center, Heidelberg, Germany.
  • Sabine Heiland
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Wolfgang Wick
    Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Martin Bendszus
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
  • Klaus Maier-Hein
    Medical Image Analysis, Division Medical Image Computing, DKFZ Heidelberg, Germany.
  • Philipp Kickingereder
    From the Department of Radiology (D.B., P.S., J.P.R., P.K., K.Y., M.F., H.P.S.), Division of Medical Image Computing (S.K., M.G., N.G., K.H.M.H.), Division of Statistics (M.W.), and Department of Medical Physics (T.A.K., F.D.), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany (D.B., H.P.S., K.H.M.H.); and Departments of Urology (J.P.R., B.H., M.H., B.A.H.) and Neuroradiology (P.K.), University of Heidelberg Medical Center, Heidelberg, Germany.

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