Conference Proceeding

Fully automatic hippocampus segmentation discriminates between early Alzheimer’s disease and normal aging

Cognitive Neurosci. & Brain Imaging Lab., Paris
06/2008; DOI:10.1109/ISBI.2008.4540941 ISBN: 978-1-4244-2002-5 pp.97 - 100 In proceeding of: Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on
Source: IEEE Xplore

ABSTRACT The hippocampus is among the first structures affected in Alzheimer's disease (AD); hippocampal MRI volumetry is a potential biomarker for AD but is hindered by the limitations of manual segmentation. We propose 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 compared with manual segmentation on data from 16 young healthy controls, with a leave-one-out strategy, and 8 AD patients. High accuracy was found for both groups (volume error 6% and 7%, overlap 87% and 86%, respectively). The resulting volumes were used to discriminate between 25 elderly subjects, 25 early AD patients and 24 patients with amnestic mild cognitive impairment (MCI). The classification proved accurate with 87% of the AD patients and 74% of the MCI patients correctly classified with respect to the elderly controls.

0 0
 · 
0 Bookmarks
 · 
45 Views
  • Source
    Chapter: A Novel Template-Based Approach to the Segmentation of the Hippocampal Region
    M. Aiello, P. Calvini, A. Chincarini, M. Esposito, G. Gemme, F. Isgrò, R. Prevete, M. Santoro, S. Squarcia
    [show abstract] [hide abstract]
    ABSTRACT: The work described in this document is part of a major work aiming at a complete pipeline for the extraction of clinical parameters from MR images of the brain, for the diagnosis of neuro-degenerative diseases. A key step in this pipeline is the identification of a box containing the hippocampus and surrounding medial temporal lobe regions from T1-weighted magnetic resonance images, with no interactive input from the user. To this end we introduced in the existing pipeline a module for the segmentation of brain tissues based on a constrained Gaussians mixture model (CGMM), and a novel method for generating templates of the hippocampus. The templates are then combined in order to obtain only one template mask. This template mask is used, with a mask of the grey matter of the brain, for determining the hippocampus. The results have been visually evaluated by a small set of experts, and have been judged as satisfactory. A complete and exhaustive evaluation of the whole system is being planned. KeywordsMagnetic resonance-Image analysis-Hippocampus segmentation
    01/2011: pages 229-246; , ISBN: 978-94-007-0010-9
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Full-text

View
0 Downloads
Available from

Keywords

16 young controls
 
16 young healthy controls
 
24 patients
 
25 elderly subjects
 
8 AD patients
 
AD patients
 
Alzheimer's disease
 
amnestic mild cognitive impairment
 
anatomical knowledge
 
hippocampal MRI volumetry
 
leave-one-out strategy
 
manual segmentation
 
MCI patients
 
probabilistic
 
Probabilistic information
 
resulting volumes
 
volume error 6%
 

M. Chupin