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Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease

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  • Pierrick Coupé
  • ,
  • Simon Fristed Eskildsen
  • José V. Manjón
  • ,
  • Vladimir S. Fonov
  • ,
  • Jens C. Pruessner
  • ,
  • Michèle Allard
  • ,
  • D. Louis Collins
Detection of Alzheimer's disease (AD) at the first stages of the pathology is an important task to accelerate the development of new therapies and improve treatment. Compared to AD detection, the prediction of AD using structural MRI at the mild cognitive impairment (MCI) or pre-MCI stage is more complex because the associated anatomical changes are more subtle. In this study, we analyzed the capability of a recently proposed method, SNIPE (Scoring by Nonlocal Image Patch Estimator), to predict AD by analyzing entorhinal cortex (EC) and hippocampus (HC) scoring over the entire ADNI database (834 scans). Detection (AD vs. CN) and prediction (progressive — pMCI vs. stable — sMCI) efficiency of SNIPE were studied using volumetric and grading biomarkers. First, our results indicate that grading-based biomarkers are more relevant for prediction than volume-based biomarkers. Second, we show that HC-based biomarkers are more important than EC-based biomarkers for prediction. Third, we demonstrate that the results obtained by SNIPE are similar to or better than results obtained in an independent study using HC volume, cortical thickness, and tensor-based morphometry, individually and in combination. Fourth, a comparison of new patch-based methods shows that the nonlocal redundancy strategy involved in SNIPE obtained similar results to a new local sparse-based approach. Finally, we present the first results of patch-based morphometry to illustrate the progression of the pathology.
Original languageEnglish
JournalNeuroImage: Clinical
Volume1
Issue1
Pages (from-to)141–152
Number of pages13
ISSN2213-1582
Publication statusPublished - 2012

    Research areas

  • Scoring, Grading, Hippocampus, Entorhinal cortex, Patient's classification, Nonlocal means estimator, Alzheimer's disease, Early detection

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