Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data.

Journal: Radiation oncology (London, England)
Published Date: Apr 20, 2020

Abstract

INTRODUCTION: Deep learning-based algorithms have demonstrated enormous performance in segmentation of medical images. We collected a dataset of multiparametric MRI and contour data acquired for use in radiosurgery, to evaluate the performance of deep convolutional neural networks (DCNN) in automatic segmentation of brain metastases (BM).

Authors

  • Khaled Bousabarah
    University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Stereotactic and Functional Neurosurgery, Cologne, Germany. khaled.bousabarah@uk-koeln.de.
  • Maximilian Ruge
  • Julia-Sarita Brand
    University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Stereotactic and Functional Neurosurgery, Cologne, Germany.
  • Mauritius Hoevels
    University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Stereotactic and Functional Neurosurgery, Cologne, Germany.
  • Daniel Rueß
    University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Stereotactic and Functional Neurosurgery, Cologne, Germany.
  • Jan Borggrefe
    Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany. jan.borggrefe@uk-koeln.de.
  • Nils Große Hokamp
    Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional Radiology, University of Cologne, Kerpener Str. 62, 50937, Cologne, Germany. Nils.Grosse-Hokamp@uk-koeln.de.
  • Veerle Visser-Vandewalle
  • David Maintz
    Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany.
  • Harald Treuer
    Department of Stereotaxy and Functional Neurosurgery, University Hospital Cologne, Cologne, Germany, harald.treuer@uk-koeln.de.
  • Martin Kocher

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