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 proceedingConference proceeding contributionpeer-review

13 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 languageEnglish
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1336-1339
Number of pages4
ISBN (Electronic)9781467364553
ISBN (Print)9781467364546, 9781467364560
DOIs
Publication statusPublished - 22 Aug 2013
Externally publishedYes
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: 7 Apr 201311 Apr 2013

Conference

Conference2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
CountryUnited States
CitySan Francisco, CA
Period7/04/1311/04/13

Keywords

  • Alzheimer's disease
  • neuroimaging
  • prediction

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