An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

NeuroImage: Clinical

Alexis Roche¹  Gunnar Krueger¹  Daniel Schmitter¹  Bénédicte Maréchal¹  Delphine Ribes¹  Meritxell Bach-Cuadra²  Philippe Maeder²  Reto Meuli²  Stefan Klöppel³  Ahmed Abdulkadir³  Cristina Granziera⁴  Alessandro Daducci⁵ 

[1] Advanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, Switzerland;Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, Switzerland;Group of Pattern Recognition and Image Processing, University of Freiburg, D-79110 Freiburg, Germany;Service of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, Switzerland;Signal Processing Laboratory 5, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, Switzerland;

关键词: Magnetic resonance imaging;    Brain morphometry;    Image segmentation;    Alzheimer's disease;    Mild cognitive impairment;    Classification;    Support vector machine;   

DOI  :  10.1016/j.nicl.2014.11.001

来源: DOAJ

【 摘 要 】

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

【 授权许可】

Unknown