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

Gerard Sanroma; Víctor Andrea; Oualid Benkarim; José Manjón; Pierrick Coupé; Oscar Camara; Gemma Piella; Miguel González Ballester

doi:10.1007/978-3-319-67434-6_9

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

Gerard Sanroma ^{1, 2} Víctor Andrea ² Oualid Benkarim ² José Manjón ³ Pierrick Coupé ^{4, 5} Oscar Camara ¹ Gemma Piella ¹ Miguel González Ballester ²

1 Universitat Pompeu Fabra [Barcelona]

2 DITIC, universitat Pompeu Fabra

3 Universidad Politécnica de Valencia

4 PICTURA

LaBRI - Laboratoire Bordelais de Recherche en Informatique

5 LaBRI - Laboratoire Bordelais de Recherche en Informatique

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

Document type :

Conference papers

Domain :

Computer Science [cs] / Medical Imaging

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https://hal.archives-ouvertes.fr/hal-01626255
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