HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download

Contributor : Pierrick Coupé Connect in order to contact the contributor
Submitted on : Monday, October 30, 2017 - 3:20:33 PM
Last modification on : Friday, January 7, 2022 - 3:55:01 AM
Long-term archiving on: : Wednesday, January 31, 2018 - 12:54:16 PM


Files produced by the author(s)




Gerard Sanroma, Víctor Andrea, Oualid Benkarim, José Manjón, Pierrick Coupé, et al.. Early Prediction of Alzheimer's Disease with Non-local Patch-Based Longitudinal Descriptors. International Workshop on Patch-based Techniques in Medical Imaging (MICCAI), Sep 2017, Québec, Canada. ⟨10.1007/978-3-319-67434-6_9⟩. ⟨hal-01626255⟩



Record views


Files downloads