Early Prediction of Alzheimer’s Disease with Non-local Patch-Based Longitudinal Descriptors

  • Gerard Sanroma
  • Víctor Andrea
  • Oualid M. Benkarim
  • José V. Manjón
  • Pierrick Coupé
  • Oscar Camara
  • Gemma Piella
  • Miguel A. González Ballester
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10530)

Abstract

Alzheimer’s disease (AD) is characterized by a progressive decline in the cognitive functions accompanied by an atrophic process which can already be observed in the early stages using magnetic resonance images (MRI). Individualized prediction of future progression to AD, when patients are still in the mild cognitive impairment (MCI) stage, has potential impact for preventive treatment. Atrophy patterns extracted from longitudinal MRI sequences provide valuable information to identify MCI patients at higher risk of developing AD in the future. We present a novel descriptor that uses the similarity between local image patches to encode local displacements due to atrophy between a pair of longitudinal MRI scans. Using a conventional logistic regression classifier, our descriptor achieves \(76\%\) accuracy in predicting which MCI patients will progress to AD up to 3 years before conversion.

Keywords

Early AD prediction Non-local patch-based label fusion Longitudinal analysis 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gerard Sanroma
    • 1
  • Víctor Andrea
    • 1
  • Oualid M. Benkarim
    • 1
  • José V. Manjón
    • 2
  • Pierrick Coupé
    • 3
    • 4
  • Oscar Camara
    • 1
  • Gemma Piella
    • 1
  • Miguel A. González Ballester
    • 1
    • 5
  1. 1.DTICUniversitat Pompeu FabraBarcelonaSpain
  2. 2.ITACAUniversitat Politécnica de ValénciaValenciaSpain
  3. 3.University of Bordeaux, LaBRI, UMR 5800TalenceFrance
  4. 4.CNRS, LaBRI, UMR 5800TalenceFrance
  5. 5.ICREABarcelonaSpain

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