A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.

Journal: Medical image analysis
Published Date: Mar 22, 2019

Abstract

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.

Authors

  • Mikael Agn
    Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark. Electronic address: miag@dtu.dk.
  • Per Munck Af Rosenschold
    Radiation Medicine Research Center, Rigshospitalet, Copenhagen, Denmark; Niels Bohr Institute, University of Copenhagen, Denmark.
  • Oula Puonti
    Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital Hvidovre, Denmark.
  • Michael J Lundemann
    Department of Oncology, Copenhagen University Hospital Rigshospitalet, Denmark.
  • Laura Mancini
    Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, UK; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, UK.
  • Anastasia Papadaki
    Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, UK; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, UK.
  • Steffi Thust
    Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK.
  • John Ashburner
    Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, University College London, UK.
  • Ian Law
    Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark.
  • Koen Van Leemput

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