Parametric surface modeling and registration for comparison of manual and automated segmentation of the hippocampus

Li Shen, Hiram A. Firpi, Andrew J. Saykin, John D. West

Research output: Contribution to journalArticle

16 Scopus citations


Accurate and efficient segmentation of the hippocampus from brain images is a challenging issue. Although experienced anatomic tracers can be reliable, manual segmentation is a time consuming process and may not be feasible for large-scale neuroimaging studies. In this article, we compare an automated method, FreeSurfer (V4), with a published manual protocol on the determination of hippocampal boundaries from magnetic resonance imaging scans, using data from an existing mild cognitive impairment/Alzheimer's disease cohort. To perform the comparison, we develop an enhanced spherical harmonic processing framework to model and register these hippocampal traces. The framework treats the two hippocampi as a single geometric configuration and extracts the positional, orientation, and shape variables in a multiobject setting. We apply this framework to register manual tracing and Free-Surfer results together and the two methods show stronger agreement on position and orientation than shape measures. Work is in progress to examine a refined FreeSurfer segmentation strategy and an improved agreement on shape features is expected.

Original languageEnglish (US)
Pages (from-to)588-595
Number of pages8
Issue number6
StatePublished - Jun 1 2009



  • Hippocampus
  • Registration
  • Segmentation
  • Shape analysis

ASJC Scopus subject areas

  • Cognitive Neuroscience

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