Comunicació presentada a: the 3rd International Workshop on Patch-based Techniques in Medical Imaging, amb conjunció amb MICCAI, celebrat a Québec, Canadà, el 14 de setembre de 2017.

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.

The first author is co-financed by the Marie Curie FP7-PEOPLE-2012-COFUND 462 Action. Grant agreement no: 600387.