Graph of brain structures grading for early detection of Alzheimer's disease

Kilian Hett (1, 2) , Vinh-Thong Ta (3) , Jose Vicente Manjon (4) , Pierrick Coupé (1, 2)

1 LaBRI - Laboratoire Bordelais de Recherche en Informatique
2 CNRS - Centre National de la Recherche Scientifique
3 Bordeaux INP - Institut Polytechnique de Bordeaux
4 ITACA

Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process. To date, subject revealed advanced brain structural alterations when the diagnosis is established. Therefore, an earlier diagnosis of this dementia is crucial although it is a challenging task. Recently, many studies have proposed biomarkers to perform early detection of Alzheimer's disease. Some of them have proposed methods based on inter-subject similarity while other approaches have investigated framework using intra-subject variability. In this work, we propose a novel framework combining both approaches within an efficient graph of brain structures grading. Subsequently, we demonstrate the competitive performance of the proposed method compared to state-of-the-art methods.

Mots clés: Intra-subject variability; Inter-subject simi- larity; Inter-subject similarity; Alzheimer's disease classification; Mild Cognitive Impairment; Patch-based grading

Domaines: Apprentissage [cs.LG]; Imagerie médicale; Traitement du signal et de l'image [eess.SP]

Kilian Hett, Vinh-Thong Ta, Jose Vicente Manjon, Pierrick Coupé. Graph of brain structures grading for early detection of Alzheimer's disease. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Sep 2018, GRANADA, Spain. ⟨hal-01830896⟩