DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning.

Journal: Acta neurochirurgica
Published Date: Dec 19, 2022

Abstract

PURPOSE: Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative magnetic resonance imaging scans, allowing us to assess the percentagewise tumor reduction after intracranial surgery for gliomas.

Authors

  • Olivier Zanier
    Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
  • Raffaele Da Mutten
    Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
  • Moira Vieli
    Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
  • Luca Regli
    Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Carlo Serra
    1Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.
  • Victor E Staartjes
    Department of Neurosurgery, Bergman Clinics, Naarden, The Netherlands; and.

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