Segmentation of hippocampus and amygdala using multi-channel landmark large deformation diffeomorphic metric mapping

Xiaoying Tang, Susumu Mori, Tilak Ratnanather, Michael I. Miller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A method to automatically segment the hippocampus and the amygdala using multi-channel large deformation diffeomorphic metric mapping (LDDMM) is proposed. T1 data from normal young subjects were used to measure the segmentation accuracy. To examine the impact of morphological abnormalities on the accuracy, the method was also tested using subjects with Alzheimer's disease (AD). The segmentation accuracy was compared with two state-of-the-art algorithms - FSL and Freesurfer. To improve the segmentation accuracy, LDDMM cascading was adopted. For normal subjects, the mean kappa overlap ratios of the automated segmentations with the manual segmentations were 0.76 and 0.84 for the hippocampus and the amygdala, respectively. For AD patients, the respective mean kappa overlap ratios were 0.76 and 0.8.

Original languageEnglish (US)
Title of host publication2012 38th Annual Northeast Bioengineering Conference, NEBEC 2012
Pages414-415
Number of pages2
DOIs
StatePublished - 2012
Event38th Annual Northeast Bioengineering Conference, NEBEC 2012 - Philadelphia, PA, United States
Duration: Mar 16 2012Mar 18 2012

Publication series

Name2012 38th Annual Northeast Bioengineering Conference, NEBEC 2012

Other

Other38th Annual Northeast Bioengineering Conference, NEBEC 2012
Country/TerritoryUnited States
CityPhiladelphia, PA
Period3/16/123/18/12

ASJC Scopus subject areas

  • Bioengineering

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