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Parametric surface modeling and registration for comparison of manual and automated segmentation of the hippocampus
Author(s) -
Shen Li,
Firpi Hiram A.,
Saykin Andrew J.,
West John D.
Publication year - 2009
Publication title -
hippocampus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.767
H-Index - 155
eISSN - 1098-1063
pISSN - 1050-9631
DOI - 10.1002/hipo.20613
Subject(s) - segmentation , computer science , orientation (vector space) , neuroimaging , artificial intelligence , parametric statistics , hippocampus , pattern recognition (psychology) , hippocampal formation , computer vision , neuroscience , psychology , mathematics , statistics , geometry
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 FreeSurfer 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. © 2009 Wiley‐Liss, Inc.