An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease
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, howe...
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doaj-d2ba29cc2f534057b2f570522cc12c372020-11-25T01:02:20ZengElsevierNeuroImage: Clinical2213-15822015-01-017C71710.1016/j.nicl.2014.11.001An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's diseaseDaniel Schmitter0Alexis Roche1Bénédicte Maréchal2Delphine Ribes3Ahmed Abdulkadir4Meritxell Bach-Cuadra5Alessandro Daducci6Cristina Granziera7Stefan Klöppel8Philippe Maeder9Reto Meuli10Gunnar Krueger11Advanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, SwitzerlandAdvanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, SwitzerlandAdvanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, SwitzerlandAdvanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, SwitzerlandGroup of Pattern Recognition and Image Processing, University of Freiburg, D-79110 Freiburg, GermanyDepartment of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, SwitzerlandSignal Processing Laboratory 5, Ecole Polytechnique Fédérale (EPFL), CH-1015 Lausanne, SwitzerlandService of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, SwitzerlandGroup of Pattern Recognition and Image Processing, University of Freiburg, D-79110 Freiburg, GermanyDepartment of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, SwitzerlandDepartment of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1015 Lausanne, SwitzerlandAdvanced Clinical Imaging Technology, Siemens Healthcare Sector, CH-1015 Lausanne, Switzerland 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. http://www.sciencedirect.com/science/article/pii/S221315821400165XMagnetic resonance imagingBrain morphometryImage segmentationAlzheimer's diseaseMild cognitive impairmentClassificationSupport vector machine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daniel Schmitter Alexis Roche Bénédicte Maréchal Delphine Ribes Ahmed Abdulkadir Meritxell Bach-Cuadra Alessandro Daducci Cristina Granziera Stefan Klöppel Philippe Maeder Reto Meuli Gunnar Krueger |
spellingShingle |
Daniel Schmitter Alexis Roche Bénédicte Maréchal Delphine Ribes Ahmed Abdulkadir Meritxell Bach-Cuadra Alessandro Daducci Cristina Granziera Stefan Klöppel Philippe Maeder Reto Meuli Gunnar Krueger An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease NeuroImage: Clinical Magnetic resonance imaging Brain morphometry Image segmentation Alzheimer's disease Mild cognitive impairment Classification Support vector machine |
author_facet |
Daniel Schmitter Alexis Roche Bénédicte Maréchal Delphine Ribes Ahmed Abdulkadir Meritxell Bach-Cuadra Alessandro Daducci Cristina Granziera Stefan Klöppel Philippe Maeder Reto Meuli Gunnar Krueger |
author_sort |
Daniel Schmitter |
title |
An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease |
title_short |
An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease |
title_full |
An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease |
title_fullStr |
An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease |
title_full_unstemmed |
An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease |
title_sort |
evaluation of volume-based morphometry for prediction of mild cognitive impairment and alzheimer's disease |
publisher |
Elsevier |
series |
NeuroImage: Clinical |
issn |
2213-1582 |
publishDate |
2015-01-01 |
description |
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.
|
topic |
Magnetic resonance imaging Brain morphometry Image segmentation Alzheimer's disease Mild cognitive impairment Classification Support vector machine |
url |
http://www.sciencedirect.com/science/article/pii/S221315821400165X |
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