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Erschienen in:

2017 | OriginalPaper | Buchkapitel

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

verfasst von : Gerard Sanroma, Víctor Andrea, Oualid M. Benkarim, José V. Manjón, Pierrick Coupé, Oscar Camara, Gemma Piella, Miguel A. González Ballester

Erschienen in: Patch-Based Techniques in Medical Imaging

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Learning-Based Estimation of Functional Correlation Tensors in White Matter for Early Diagnosis of Mild Cognitive Impairment

Nächstes Kapitel Adaptive Fusion of Texture-Based Grading: Application to Alzheimer’s Disease Detection

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More details at: https://sites.google.com/site/machinelearning4mcitoad/.

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Titel: Early Prediction of Alzheimer’s Disease with Non-local Patch-Based Longitudinal Descriptors
verfasst von: Gerard Sanroma
Víctor Andrea
Oualid M. Benkarim
José V. Manjón
Pierrick Coupé
Oscar Camara
Gemma Piella
Miguel A. González Ballester
Verlag: Springer International Publishing
Buch: Patch-Based Techniques in Medical Imaging
Print ISBN: 978-3-319-67433-9

Electronic ISBN: 978-3-319-67434-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-67434-6_9

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