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Early Diagnosis of Alzheimer’s Disease Using Subject-Specific Models of FDG-PET Data

Ninon Burgos 1 Jorge Samper-González 1 Jorge M. Cardoso 2 Stanley Durrleman 1 Sébastien Ourselin 2 Olivier Colliot 1 
1 ARAMIS - Algorithms, models and methods for images and signals of the human brain
UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute, Inria de Paris
Abstract : Background: In machine learning classification methods developed for dementia studies, neuroimaging features, e.g. glucose consumption extracted from PET images, are often used to draw the border that differentiates normality from abnormality. However, these features are affected by the anatomical variability present in the population, which acts as a confounding factor making the task of finding the frontier (i.e. the decision function) between normality and abnormality very challenging.
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Submitted on : Wednesday, October 25, 2017 - 10:14:14 AM
Last modification on : Friday, August 5, 2022 - 3:41:48 AM
Long-term archiving on: : Friday, January 26, 2018 - 12:37:22 PM


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Ninon Burgos, Jorge Samper-González, Jorge M. Cardoso, Stanley Durrleman, Sébastien Ourselin, et al.. Early Diagnosis of Alzheimer’s Disease Using Subject-Specific Models of FDG-PET Data. AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.1-2, ⟨10.1016/j.jalz.2017.06.1618⟩. ⟨hal-01621383⟩



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