Atlas-Based Hippocampus Segmentation In Alzheimer's Disease and Mild Cognitive Impairment

Owen Carmichael, Howard Aizenstein, Simon W. Davis, Jim Becker, Paul M. Thompson, Carolyn Meltzer, and Yanxi Liu
tech. report CMU-RI-TR-04-53, Robotics Institute, Carnegie Mellon University, December, 2004


Download
  • Adobe portable document format (pdf) (4MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Purpose. To assess the performance of standard image registration techniques for automated MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer?s Disease (AD) and mild cognitive impairment (MCI). Methods. Structural MR images of 54 age- and gender-matched healthy elderly individuals, subjects with probable AD, and subjects with MCI were collected at the University of Pittsburgh Alzheimer?s Disease Research Center. Hippocampi in subject images were automatically segmented by using AIR, SPM, FLIRT, and the fully-deformable method of Chen to align the images to the Harvard atlas, MNI atlas, and randomly-selected, manually-labeled subject images. Mixed-effects statistical models analyzed the effects of side of the brain, disease state, registration method, choice of atlas, and manual tracing protocol on the agreement between automated segmentations and expert manual segmentations. Results. Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations. Automated-manual agreement between Chen?s method and expert manual segmentations were competitive with manual-manual agreement. Segmentations of the right hippocampus were more consistent with manual segmentations than those of the left. Automated-manual agreement was significantly lower in AD brains than MCI or controls. Automated segmentations based on registration with a randomly-selected subject image were more consistent with manual segmentations than those based on registration with the Harvard or MNI atlas. The manual tracing protocol was a significant source of variation in automated-manual agreement.

Keywords
segmentation, medical image analysis, hippocampus, Alzheimer's Disease

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Lab(s) / Group(s): Biomedical Image Analysis
Associated Project(s): Predicting Risk of Alzheimer\'s Disease From Shape Features
Number of pages: 45

Text Reference
Owen Carmichael, Howard Aizenstein, Simon W. Davis, Jim Becker, Paul M. Thompson, Carolyn Meltzer, and Yanxi Liu, "Atlas-Based Hippocampus Segmentation In Alzheimer's Disease and Mild Cognitive Impairment," tech. report CMU-RI-TR-04-53, Robotics Institute, Carnegie Mellon University, December, 2004

BibTeX Reference
@techreport{Carmichael_2004_4866,
   author = "Owen Carmichael and Howard Aizenstein and Simon W. Davis and Jim Becker and Paul M. Thompson and Carolyn Meltzer and Yanxi Liu",
   title = "Atlas-Based Hippocampus Segmentation In Alzheimer's Disease and Mild Cognitive Impairment",
   booktitle = "",
   institution = "Robotics Institute",
   month = "December",
   year = "2004",
   number= "CMU-RI-TR-04-53",
   address= "Pittsburgh, PA",
}