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

Abstract

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.

Figure 1

Initialisation: a. extraction of the bounding boxes from the probabilistic atlases, b. extraction of the initial objects from each probabilistic atlas through conditional pruning.

Figure 2

3D-renderings of manual, atlas-derived and automatic segmentations, overlap between segmentations (manual segmentations in shades of grey) and probabilistic atlases, for the best and worst results amongst 8 patients with AD.

Figure 3

Experiment 1: Automatically computed (left+right) volumes for the CN (red + for correct segmentations, * for acceptable segmentations and - for the unsatisfactory segmentation) and AD (blue circles for correct segmentations, squares for acceptable segmentations and triangles for unsatisfactory segmentations) as a function of age.

Figure 4

Experiment 3: Automatically computed volumes corresponding to the classification experiments: a: CN (red crosses) and AD, b. CN (red crosses) and MCI, c. CN at 18 months (red crosses) and MCI converting at 18 months, d. MCI not converting (red crosses) and MCI converting. {x, disc} for correct segmentations, {*, square} for acceptable segmentations and {−, triangle} for unsatisfactory segmentations