Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images

Jun Zhang; Mingxia Liu; Le An; Yaozong Gao; Dinggang Shen

doi:10.1007/978-3-319-61188-4_4

Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images

Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) Highlevel statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.

Original language	English
Title of host publication	Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers
Editors	Tal Arbel, Georg Langs, Mark Jenkinson, Bjoern Menze, William M. Wells III, Albert C.S. Chung, B. Michael Kelm, Weidong Cai, Albert Montillo, Dimitris Metaxas, M. Jorge Cardoso, Shaoting Zhang, Annemie Ribbens, Henning Muller
Publisher	Springer Verlag
Pages	35-45
Number of pages	11
ISBN (Print)	9783319611877
DOIs	https://doi.org/10.1007/978-3-319-61188-4_4
Publication status	Published - 2017
Event	International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 2016 Oct 21 → 2016 Oct 21

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10081 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/Territory	Greece
City	Athens
Period	16/10/21 → 16/10/21

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-61188-4_4

Cite this

Zhang, J., Liu, M., An, L., Gao, Y., & Shen, D. (2017). Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. In T. Arbel, G. Langs, M. Jenkinson, B. Menze, W. M. Wells III, A. C. S. Chung, B. M. Kelm, W. Cai, A. Montillo, D. Metaxas, M. J. Cardoso, S. Zhang, A. Ribbens, & H. Muller (Eds.), Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers (pp. 35-45). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10081 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61188-4_4

Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. / Zhang, Jun; Liu, Mingxia; An, Le et al.

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. ed. / Tal Arbel; Georg Langs; Mark Jenkinson; Bjoern Menze; William M. Wells III; Albert C.S. Chung; B. Michael Kelm; Weidong Cai; Albert Montillo; Dimitris Metaxas; M. Jorge Cardoso; Shaoting Zhang; Annemie Ribbens; Henning Muller. Springer Verlag, 2017. p. 35-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10081 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, J, Liu, M, An, L, Gao, Y & Shen, D 2017, Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. in T Arbel, G Langs, M Jenkinson, B Menze, WM Wells III, ACS Chung, BM Kelm, W Cai, A Montillo, D Metaxas, MJ Cardoso, S Zhang, A Ribbens & H Muller (eds), Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10081 LNCS, Springer Verlag, pp. 35-45, International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, Athens, Greece, 16/10/21. https://doi.org/10.1007/978-3-319-61188-4_4

Zhang J, Liu M, An L, Gao Y, Shen D. Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. In Arbel T, Langs G, Jenkinson M, Menze B, Wells III WM, Chung ACS, Kelm BM, Cai W, Montillo A, Metaxas D, Cardoso MJ, Zhang S, Ribbens A, Muller H, editors, Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. Springer Verlag. 2017. p. 35-45. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61188-4_4

Zhang, Jun ; Liu, Mingxia ; An, Le et al. / Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers. editor / Tal Arbel ; Georg Langs ; Mark Jenkinson ; Bjoern Menze ; William M. Wells III ; Albert C.S. Chung ; B. Michael Kelm ; Weidong Cai ; Albert Montillo ; Dimitris Metaxas ; M. Jorge Cardoso ; Shaoting Zhang ; Annemie Ribbens ; Henning Muller. Springer Verlag, 2017. pp. 35-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3850f2f1378e49699eab8a4ba9992953,

title = "Landmark-based alzheimer{\textquoteright}s disease diagnosis using longitudinal structural MR images",

abstract = "In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) Highlevel statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.",

author = "Jun Zhang and Mingxia Liu and Le An and Yaozong Gao and Dinggang Shen",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 ; Conference date: 21-10-2016 Through 21-10-2016",

year = "2017",

doi = "10.1007/978-3-319-61188-4_4",

language = "English",

isbn = "9783319611877",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "35--45",

editor = "Tal Arbel and Georg Langs and Mark Jenkinson and Bjoern Menze and {Wells III}, {William M.} and Chung, {Albert C.S.} and Kelm, {B. Michael} and Weidong Cai and Albert Montillo and Dimitris Metaxas and Cardoso, {M. Jorge} and Shaoting Zhang and Annemie Ribbens and Henning Muller",

booktitle = "Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers",

}

TY - GEN

T1 - Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images

AU - Zhang, Jun

AU - Liu, Mingxia

AU - An, Le

AU - Gao, Yaozong

AU - Shen, Dinggang

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) Highlevel statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.

AB - In this paper, we propose a landmark-based feature extraction method for AD diagnosis using longitudinal structural MR images, which requires no nonlinear registration or tissue segmentation in the application stage and is robust to the inconsistency among longitudinal scans. Specifically, (1) the discriminative landmarks are first automatically discovered from the whole brain, which can be efficiently localized using a fast landmark detection method for the testing images; (2) Highlevel statistical spatial features and contextual longitudinal features are then extracted based on those detected landmarks. Using the spatial and longitudinal features, a linear support vector machine (SVM) is adopted for distinguishing AD subjects from healthy controls (HCs) and also mild cognitive impairment (MCI) subjects from HCs, respectively. Experimental results demonstrate the competitive classification accuracies, as well as a promising computational efficiency.

UR - http://www.scopus.com/inward/record.url?scp=85025147054&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-61188-4_4

DO - 10.1007/978-3-319-61188-4_4

M3 - Conference contribution

AN - SCOPUS:85025147054

SN - 9783319611877

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

SP - 35

EP - 45

BT - Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging - MICCAI 2016 International Workshops, MCV and BAMBI, Revised Selected Papers

A2 - Arbel, Tal

A2 - Langs, Georg

A2 - Jenkinson, Mark

A2 - Menze, Bjoern

A2 - Wells III, William M.

A2 - Chung, Albert C.S.

A2 - Kelm, B. Michael

A2 - Cai, Weidong

A2 - Montillo, Albert

A2 - Metaxas, Dimitris

A2 - Cardoso, M. Jorge

A2 - Zhang, Shaoting

A2 - Ribbens, Annemie

A2 - Muller, Henning

PB - Springer Verlag

T2 - International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016

Y2 - 21 October 2016 through 21 October 2016

ER -

Landmark-based alzheimer’s disease diagnosis using longitudinal structural MR images

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this