Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

Journal: BMC medical imaging
Published Date: May 4, 2017

Abstract

BACKGROUND: Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments.

Authors

  • Nicolas Sauwen
    Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KULeuven, Kasteelpark Arenberg, Leuven, Belgium. nicolas.sauwen@kuleuven.be.
  • Marjan Acou
    Department of Radiology, Ghent University Hospital, De Pintelaan 185, Ghent, 9000, Belgium.
  • Diana M Sima
    STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium.
  • Jelle Veraart
    Department of Physics, iMinds Vision Lab, University of Antwerp, Edegemsesteenweg 200-240, Antwerp, 2610, Belgium.
  • Frederik Maes
    Department of Electrical Engineering (ESAT/PSI), KU Leuven, Kasteelpark Arenberg 10/2446, 3001, Leuven, Belgium; Medical Imaging Research Center (MIRC), UZ Leuven, Herestraat 49, 3000, Leuven, Belgium. Electronic address: frederik.maes@kuleuven.be.
  • Uwe Himmelreich
    Department of Imaging and Pathology, KU Leuven, Leuven, Belgium.
  • Eric Achten
    Department of Radiology, Ghent University Hospital, De Pintelaan 185, Ghent, 9000, Belgium.
  • Sabine Van Huffel
    Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KULeuven, Kasteelpark Arenberg, Leuven, Belgium.

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