Cross validation of experts versus registration methods for target localization in deep brain stimulation

  • Authors:

  • F. Javier Sánchez Castro;Claudio Pollo;Reto Meuli;Philippe Maeder;Meritxell Bach Cuadra;Olivier Cuisenaire;Jean-Guy Villemure;Jean-Philippe Thiran

  • Affiliations:

  • Signal Processing Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Signal Processing Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Departments of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Switzerland;Departments of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Switzerland;Signal Processing Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Signal Processing Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Departments of Neurosurgery, Centre Hospitalier Universitaire Vaudois (CHUV), Switzerland;Signal Processing Institute, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

  • Venue:
  • MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
  • Year:
  • 2005

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Abstract

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatment of Parkinson’s disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different registration algorithms and to evaluate their ability to locate the STN automatically. Using this scheme we can evaluate the expert variability against the error of the algorithms and we demonstrate that automatic STN location is possible and as accurate as the methods currently used.