Semi-automated basal ganglia segmentation using large deformation diffeomorphic metric mapping

  • Authors:

  • Ali Khan;Elizabeth Aylward;Patrick Barta;Michael Miller;M. Faisal Beg

  • Affiliations:

  • School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada;Department of Radiology and Psychiatry, University of Washington, Seattle, WA;Department of Psychiatry and Behavioral Sciences, Division of Psychiatric Neuro-Imaging, The Johns Hopkins University School of Medicine, Baltimore, MD;Center for Imaging Science, The Johns Hopkins University, Baltimore, MD;School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

  • 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

This paper investigates the techniques required to produce accurate and reliable segmentations via grayscale image matching. Finding a large deformation, dense, non-rigid transformation from a template image to a target image allows us to map a template segmentation to the target image space, and therefore compute the target image segmentation and labeling. We outline a semi-automated procedure involving landmark and image intensity-based matching via the large deformation diffeomorphic mapping metric (LDDMM) algorithm. Our method is applied specifically to the segmentation of the caudate nucleus in pre- and post-symptomatic Huntington’s Disease (HD) patients. Our accuracy is compared against gold-standard manual segmentations and various automated segmentation tools through the use of several error metrics.