Computational analysis of LDDMM for brain mapping

Can Ceritoglu, Xiaoying Tang, Margaret Chow, Darian Hadjiabadi, Damish Shah, Timothy Brown, Muhammad H. Burhanullah, Huong Trinh, John T. Hsu, Katarina A. Ament, Deana Crocetti, Susumu Mori, Stewart H. Mostofsky, Steven Yantis, Michael I. Miller, J. Tilak Ratnanather

Research output: Contribution to journalArticle

Abstract

One goal of computational anatomy (CA) is to develop tools to accurately segment brain structures in healthy and diseased subjects. In this paper, we examine the performance and complexity of such segmentation in the framework of the large deformation diffeomorphic metric mapping (LDDMM) registration method with reference to atlases and parameters. First we report the application of a multi-atlas segmentation approach to define basal ganglia structures in healthy and diseased kids' brains. The segmentation accuracy of the multi-atlas approach is compared with the single atlas LDDMM implementation and two state-of-the-art segmentation algorithms-Freesurfer and FSL-by computing the overlap errors between automatic and manual segmentations of the six basal ganglia nuclei in healthy subjects as well as subjects with diseases including ADHD and Autism. The high accuracy of multi-atlas segmentation is obtained at the cost of increasing the computational complexity because of the calculations necessary between the atlases and a subject. Second, we examine the effect of parameters on total LDDMM computation time and segmentation accuracy for basal ganglia structures. Single atlas LDDMM method is used to automatically segment the structures in a population of 16 subjects using different sets of parameters. The results show that a cascade approach and using fewer time steps can reduce computational complexity as much as five times while maintaining reliable segmentations.

Original languageEnglish (US)
Article numberArticle 151
JournalFrontiers in Neuroscience
Issue number7 AUG
DOIs
StatePublished - Jan 1 2013

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Keywords

  • Brain mapping
  • Computational anatomy
  • LDDMM
  • Subcortical segmentation

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Ceritoglu, C., Tang, X., Chow, M., Hadjiabadi, D., Shah, D., Brown, T., Burhanullah, M. H., Trinh, H., Hsu, J. T., Ament, K. A., Crocetti, D., Mori, S., Mostofsky, S. H., Yantis, S., Miller, M. I., & Tilak Ratnanather, J. (2013). Computational analysis of LDDMM for brain mapping. Frontiers in Neuroscience, (7 AUG), [Article 151]. https://doi.org/10.3389/fnins.2013.00151