Multi-atlas based representations for Alzheimer's disease diagnosis

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

36 Citations (Scopus)

Abstract

Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods.

Original languageEnglish
Pages (from-to)5052-5070
Number of pages19
JournalHuman Brain Mapping
Volume35
Issue number10
DOIs
Publication statusPublished - 2014

Fingerprint

Atlases
Alzheimer Disease
Brain
Prodromal Symptoms
Neuroimaging
Magnetic Resonance Spectroscopy
Databases

Keywords

  • AD diagnosis
  • Brain classification
  • Multi-atlas based morphometry

ASJC Scopus subject areas

  • Clinical Neurology
  • Anatomy
  • Neurology
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Multi-atlas based representations for Alzheimer's disease diagnosis. / Alzheimer's Disease Neuroimaging Initiative.

In: Human Brain Mapping, Vol. 35, No. 10, 2014, p. 5052-5070.

Research output: Contribution to journalArticle

Alzheimer's Disease Neuroimaging Initiative 2014, 'Multi-atlas based representations for Alzheimer's disease diagnosis', Human Brain Mapping, vol. 35, no. 10, pp. 5052-5070. https://doi.org/10.1002/hbm.22531
Alzheimer's Disease Neuroimaging Initiative. / Multi-atlas based representations for Alzheimer's disease diagnosis. In: Human Brain Mapping. 2014 ; Vol. 35, No. 10. pp. 5052-5070.
@article{4ab67765ba294f9ead9ba9873a6ca14e,
title = "Multi-atlas based representations for Alzheimer's disease diagnosis",
abstract = "Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64{\%} for AD/NC classification and 72.41{\%} for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods.",
keywords = "AD diagnosis, Brain classification, Multi-atlas based morphometry",
author = "{Alzheimer's Disease Neuroimaging Initiative} and Rui Min and Guorong Wu and Jian Cheng and Qian Wang and Dinggang Shen",
year = "2014",
doi = "10.1002/hbm.22531",
language = "English",
volume = "35",
pages = "5052--5070",
journal = "Human Brain Mapping",
issn = "1065-9471",
publisher = "Wiley-Liss Inc.",
number = "10",

}

TY - JOUR

T1 - Multi-atlas based representations for Alzheimer's disease diagnosis

AU - Alzheimer's Disease Neuroimaging Initiative

AU - Min, Rui

AU - Wu, Guorong

AU - Cheng, Jian

AU - Wang, Qian

AU - Shen, Dinggang

PY - 2014

Y1 - 2014

N2 - Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods.

AB - Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods.

KW - AD diagnosis

KW - Brain classification

KW - Multi-atlas based morphometry

UR - http://www.scopus.com/inward/record.url?scp=84927578310&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84927578310&partnerID=8YFLogxK

U2 - 10.1002/hbm.22531

DO - 10.1002/hbm.22531

M3 - Article

C2 - 24753060

AN - SCOPUS:84927578310

VL - 35

SP - 5052

EP - 5070

JO - Human Brain Mapping

JF - Human Brain Mapping

SN - 1065-9471

IS - 10

ER -