Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI

Hippocampus. 2009 Jun;19(6):579-87. doi: 10.1002/hipo.20626.


The hippocampus is among the first structures affected in Alzheimer's disease (AD). Hippocampal magnetic resonance imaging volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We proposed a fully automatic method using probabilistic and anatomical priors for hippocampus segmentation. Probabilistic information is derived from 16 young controls and anatomical knowledge is modeled with automatically detected landmarks. The results were previously evaluated by comparison with manual segmentation on data from the 16 young healthy controls, with a leave-one-out strategy, and eight patients with AD. High accuracy was found for both groups (volume error 6 and 7%, overlap 87 and 86%, respectively). In this article, the method was used to segment 145 patients with AD, 294 patients with mild cognitive impairment (MCI), and 166 elderly normal subjects from the Alzheimer's Disease Neuroimaging Initiative database. On the basis of a qualitative rating protocol, the segmentation proved acceptable in 94% of the cases. We used the obtained hippocampal volumes to automatically discriminate between AD patients, MCI patients, and elderly controls. The classification proved accurate: 76% of the patients with AD and 71% of the MCI converting to AD before 18 months were correctly classified with respect to the elderly controls, using only hippocampal volume.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Age Factors
  • Aged
  • Algorithms
  • Alzheimer Disease / diagnosis*
  • Alzheimer Disease / pathology*
  • Automation
  • Cognition Disorders / diagnosis*
  • Cognition Disorders / pathology*
  • Databases, Factual
  • Hippocampus / pathology*
  • Humans
  • Imaging, Three-Dimensional
  • Magnetic Resonance Imaging
  • Models, Anatomic
  • Organ Size
  • Probability