Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine

Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Dinggang Shen, Guorong Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)

Abstract

The diagnosis of Alzheimer’s disease (AD) from neuroimaging data at the pre-clinical stage has been intensively investigated because of the immense social and economic cost. In the past decade,computational approaches on longitudinal image sequences have been actively investigated with special attention to Mild Cognitive Impairment (MCI),which is an intermediate stage between normal control (NC) and AD. However,current state-of-the-art diagnosis methods have limited power in clinical practice,due to the excessive requirements such as equal and immoderate number of scans in longitudinal imaging data. More critically,very few methods are specifically designed for the early alarm of AD uptake. To address these limitations,we propose a flexible spatial-temporal solution for early detection of AD by recognizing abnormal structure changes from longitudinal MR image sequence. Specifically,our method is leveraged by the non-reversible nature of AD progression. We employ temporally structured SVM to accurately alarm AD at early stage by enforcing the monotony on classification result to avoid unrealistic and inconsistent diagnosis result along time. Furthermore,in order to select best features which can well collaborate with the classifier,we present as joint feature selection and classification framework. The evaluation on more than 150 longitudinal subjects from ADNI dataset shows that our method is able to alarm the conversion of AD 12 months prior to the clinical diagnosis with at least 82.5 % accuracy. It is worth noting that our proposed method works on widely used MR images and does not have restriction on the number of scans in the longitudinal sequence,which is very attractive to real clinical practice.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages264-272
Number of pages9
Volume9900 LNCS
ISBN (Print)9783319467191
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 212016 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period16/10/2116/10/21

Fingerprint

Alzheimer's Disease
Feature Selection
Support vector machines
Feature extraction
Support Vector Machine
Image Sequence
Neuroimaging
Progression
Inconsistent
Classifiers
Classifier
Imaging
Economics
Restriction
Imaging techniques
Requirements
Evaluation
Costs

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Zhu, Y., Zhu, X., Kim, M., Shen, D., & Wu, G. (2016). Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9900 LNCS, pp. 264-272). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46720-7_31

Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. / Zhu, Yingying; Zhu, Xiaofeng; Kim, Minjeong; Shen, Dinggang; Wu, Guorong.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. p. 264-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9900 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zhu, Y, Zhu, X, Kim, M, Shen, D & Wu, G 2016, Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. vol. 9900 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9900 LNCS, Springer Verlag, pp. 264-272, 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/21. https://doi.org/10.1007/978-3-319-46720-7_31
Zhu Y, Zhu X, Kim M, Shen D, Wu G. Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS. Springer Verlag. 2016. p. 264-272. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-46720-7_31
Zhu, Yingying ; Zhu, Xiaofeng ; Kim, Minjeong ; Shen, Dinggang ; Wu, Guorong. / Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9900 LNCS Springer Verlag, 2016. pp. 264-272 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{aa8cb1f01d4d4d33abc445b7af1f313d,
title = "Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine",
abstract = "The diagnosis of Alzheimer’s disease (AD) from neuroimaging data at the pre-clinical stage has been intensively investigated because of the immense social and economic cost. In the past decade,computational approaches on longitudinal image sequences have been actively investigated with special attention to Mild Cognitive Impairment (MCI),which is an intermediate stage between normal control (NC) and AD. However,current state-of-the-art diagnosis methods have limited power in clinical practice,due to the excessive requirements such as equal and immoderate number of scans in longitudinal imaging data. More critically,very few methods are specifically designed for the early alarm of AD uptake. To address these limitations,we propose a flexible spatial-temporal solution for early detection of AD by recognizing abnormal structure changes from longitudinal MR image sequence. Specifically,our method is leveraged by the non-reversible nature of AD progression. We employ temporally structured SVM to accurately alarm AD at early stage by enforcing the monotony on classification result to avoid unrealistic and inconsistent diagnosis result along time. Furthermore,in order to select best features which can well collaborate with the classifier,we present as joint feature selection and classification framework. The evaluation on more than 150 longitudinal subjects from ADNI dataset shows that our method is able to alarm the conversion of AD 12 months prior to the clinical diagnosis with at least 82.5 {\%} accuracy. It is worth noting that our proposed method works on widely used MR images and does not have restriction on the number of scans in the longitudinal sequence,which is very attractive to real clinical practice.",
author = "Yingying Zhu and Xiaofeng Zhu and Minjeong Kim and Dinggang Shen and Guorong Wu",
year = "2016",
doi = "10.1007/978-3-319-46720-7_31",
language = "English",
isbn = "9783319467191",
volume = "9900 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "264--272",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",

}

TY - GEN

T1 - Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine

AU - Zhu, Yingying

AU - Zhu, Xiaofeng

AU - Kim, Minjeong

AU - Shen, Dinggang

AU - Wu, Guorong

PY - 2016

Y1 - 2016

N2 - The diagnosis of Alzheimer’s disease (AD) from neuroimaging data at the pre-clinical stage has been intensively investigated because of the immense social and economic cost. In the past decade,computational approaches on longitudinal image sequences have been actively investigated with special attention to Mild Cognitive Impairment (MCI),which is an intermediate stage between normal control (NC) and AD. However,current state-of-the-art diagnosis methods have limited power in clinical practice,due to the excessive requirements such as equal and immoderate number of scans in longitudinal imaging data. More critically,very few methods are specifically designed for the early alarm of AD uptake. To address these limitations,we propose a flexible spatial-temporal solution for early detection of AD by recognizing abnormal structure changes from longitudinal MR image sequence. Specifically,our method is leveraged by the non-reversible nature of AD progression. We employ temporally structured SVM to accurately alarm AD at early stage by enforcing the monotony on classification result to avoid unrealistic and inconsistent diagnosis result along time. Furthermore,in order to select best features which can well collaborate with the classifier,we present as joint feature selection and classification framework. The evaluation on more than 150 longitudinal subjects from ADNI dataset shows that our method is able to alarm the conversion of AD 12 months prior to the clinical diagnosis with at least 82.5 % accuracy. It is worth noting that our proposed method works on widely used MR images and does not have restriction on the number of scans in the longitudinal sequence,which is very attractive to real clinical practice.

AB - The diagnosis of Alzheimer’s disease (AD) from neuroimaging data at the pre-clinical stage has been intensively investigated because of the immense social and economic cost. In the past decade,computational approaches on longitudinal image sequences have been actively investigated with special attention to Mild Cognitive Impairment (MCI),which is an intermediate stage between normal control (NC) and AD. However,current state-of-the-art diagnosis methods have limited power in clinical practice,due to the excessive requirements such as equal and immoderate number of scans in longitudinal imaging data. More critically,very few methods are specifically designed for the early alarm of AD uptake. To address these limitations,we propose a flexible spatial-temporal solution for early detection of AD by recognizing abnormal structure changes from longitudinal MR image sequence. Specifically,our method is leveraged by the non-reversible nature of AD progression. We employ temporally structured SVM to accurately alarm AD at early stage by enforcing the monotony on classification result to avoid unrealistic and inconsistent diagnosis result along time. Furthermore,in order to select best features which can well collaborate with the classifier,we present as joint feature selection and classification framework. The evaluation on more than 150 longitudinal subjects from ADNI dataset shows that our method is able to alarm the conversion of AD 12 months prior to the clinical diagnosis with at least 82.5 % accuracy. It is worth noting that our proposed method works on widely used MR images and does not have restriction on the number of scans in the longitudinal sequence,which is very attractive to real clinical practice.

UR - http://www.scopus.com/inward/record.url?scp=84996569803&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84996569803&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-46720-7_31

DO - 10.1007/978-3-319-46720-7_31

M3 - Conference contribution

AN - SCOPUS:84996569803

SN - 9783319467191

VL - 9900 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 264

EP - 272

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings

PB - Springer Verlag

ER -