Neuroimaging biomarker based prediction of Alzheimer's disease severity with optimized graph construction

Sidong Liu; Weidong Cai; Lingfeng Wen; Dagan Feng

doi:10.1109/ISBI.2013.6556779

Neuroimaging biomarker based prediction of Alzheimer's disease severity with optimized graph construction

Sidong Liu, Weidong Cai, Lingfeng Wen, Dagan Feng

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

16 Citations (Scopus)

Abstract

The prediction of Alzheimer's disease (AD) severity is very important in AD diagnosis and patient care, especially for patients at early stage when clinical intervention is most effective and no irreversible damages have been formed to brains. To achieve accurate diagnosis of AD and identify the subjects who have higher risk to convert to AD, we proposed an AD severity prediction method based on the neuroimaging predictors evaluated by the region-wise atrophy patterns. The proposed method introduced a global cost function that encodes the empirical conversion rates for subjects at different progression stages from normal aging through mild cogitative impairment (MCI) to AD, based on the classic graph cut algorithm. Experimental results on ADNI baseline dataset of 758 subjects validated the efficacy of the proposed method.

Original language	English
Title of host publication	ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1336-1339
Number of pages	4
ISBN (Electronic)	9781467364553
ISBN (Print)	9781467364546, 9781467364560
DOIs	https://doi.org/10.1109/ISBI.2013.6556779
Publication status	Published - 22 Aug 2013
Externally published	Yes
Event	2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States Duration: 7 Apr 2013 → 11 Apr 2013

Conference

Conference	2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Country/Territory	United States
City	San Francisco, CA
Period	7/04/13 → 11/04/13

Keywords

Alzheimer's disease
neuroimaging
prediction

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10.1109/ISBI.2013.6556779

Cite this

Liu, S., Cai, W., Wen, L., & Feng, D. (2013). Neuroimaging biomarker based prediction of Alzheimer's disease severity with optimized graph construction. In ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1336-1339). Article 6556779 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ISBI.2013.6556779

@inproceedings{7dd70c3beb3d4819904abc0896fcf83a,

title = "Neuroimaging biomarker based prediction of Alzheimer's disease severity with optimized graph construction",

abstract = "The prediction of Alzheimer's disease (AD) severity is very important in AD diagnosis and patient care, especially for patients at early stage when clinical intervention is most effective and no irreversible damages have been formed to brains. To achieve accurate diagnosis of AD and identify the subjects who have higher risk to convert to AD, we proposed an AD severity prediction method based on the neuroimaging predictors evaluated by the region-wise atrophy patterns. The proposed method introduced a global cost function that encodes the empirical conversion rates for subjects at different progression stages from normal aging through mild cogitative impairment (MCI) to AD, based on the classic graph cut algorithm. Experimental results on ADNI baseline dataset of 758 subjects validated the efficacy of the proposed method.",

keywords = "Alzheimer's disease, neuroimaging, prediction",

author = "Sidong Liu and Weidong Cai and Lingfeng Wen and Dagan Feng",

year = "2013",

month = aug,

day = "22",

doi = "10.1109/ISBI.2013.6556779",

language = "English",

isbn = "9781467364546",

pages = "1336--1339",

booktitle = "ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 ; Conference date: 07-04-2013 Through 11-04-2013",

}

Liu, S, Cai, W, Wen, L & Feng, D 2013, Neuroimaging biomarker based prediction of Alzheimer's disease severity with optimized graph construction. in ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro., 6556779, Institute of Electrical and Electronics Engineers (IEEE), pp. 1336-1339, 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013, San Francisco, CA, United States, 7/04/13. https://doi.org/10.1109/ISBI.2013.6556779

Neuroimaging biomarker based prediction of Alzheimer's disease severity with optimized graph construction. / Liu, Sidong; Cai, Weidong; Wen, Lingfeng et al.
ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro. Institute of Electrical and Electronics Engineers (IEEE), 2013. p. 1336-1339 6556779.

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

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AU - Cai, Weidong

AU - Wen, Lingfeng

AU - Feng, Dagan

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N2 - The prediction of Alzheimer's disease (AD) severity is very important in AD diagnosis and patient care, especially for patients at early stage when clinical intervention is most effective and no irreversible damages have been formed to brains. To achieve accurate diagnosis of AD and identify the subjects who have higher risk to convert to AD, we proposed an AD severity prediction method based on the neuroimaging predictors evaluated by the region-wise atrophy patterns. The proposed method introduced a global cost function that encodes the empirical conversion rates for subjects at different progression stages from normal aging through mild cogitative impairment (MCI) to AD, based on the classic graph cut algorithm. Experimental results on ADNI baseline dataset of 758 subjects validated the efficacy of the proposed method.

AB - The prediction of Alzheimer's disease (AD) severity is very important in AD diagnosis and patient care, especially for patients at early stage when clinical intervention is most effective and no irreversible damages have been formed to brains. To achieve accurate diagnosis of AD and identify the subjects who have higher risk to convert to AD, we proposed an AD severity prediction method based on the neuroimaging predictors evaluated by the region-wise atrophy patterns. The proposed method introduced a global cost function that encodes the empirical conversion rates for subjects at different progression stages from normal aging through mild cogitative impairment (MCI) to AD, based on the classic graph cut algorithm. Experimental results on ADNI baseline dataset of 758 subjects validated the efficacy of the proposed method.

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KW - neuroimaging

KW - prediction

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DO - 10.1109/ISBI.2013.6556779

M3 - Conference proceeding contribution

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BT - ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging

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Y2 - 7 April 2013 through 11 April 2013

ER -

Neuroimaging biomarker based prediction of Alzheimer's disease severity with optimized graph construction

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