deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks.

Journal: Computers in biology and medicine
Published Date: Jul 12, 2024

Abstract

BACKGROUND: Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods.

Authors

  • Lukas Fisch
    Institute for Translational Psychiatry, University of Münster, Münster, Germany.
  • Stefan Zumdick
    University of Münster, Institute for Translational Psychiatry, Münster, Germany.
  • Carlotta Barkhau
    Symbic GmbH, Osnabrück, Germany.
  • Daniel Emden
    Institute for Translational Psychiatry, University of Münster, Münster, Germany.
  • Jan Ernsting
    Institute for Translational Psychiatry, University of Münster, Münster, Germany.
  • Ramona Leenings
    Institute for Translational Psychiatry, University of Münster, Münster, Germany.
  • Kelvin Sarink
    Institute for Translational Psychiatry, University of Münster, Münster, Germany.
  • Nils R Winter
    Department of Psychiatry, University of Muenster, Münster, Germany.
  • Benjamin Risse
    Faculty of Mathematics and Computer Science, University of Münster, Münster, Germany.
  • Udo Dannlowski
    Department of Psychiatry and Psychotherapy, University of Münster, Germany.
  • Tim Hahn

Keywords

External Resources

Popular Topics
Recent Journals