Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
Alzheimer's disease (AD) is a major neurodegenerative condition that affects
millions around the world. As one of the main biomarkers in the AD diagnosis
procedure, brain amyloid positivity is typically identified by positron
emission tomography (PET), which is costly and invasive. Brain structural
magnetic resonance imaging (sMRI) may provide a safer and more convenient
solution for the AD diagnosis. Recent advances in geometric deep learning have
facilitated sMRI analysis and early diagnosis of AD. However, determining AD
pathology, such as brain amyloid deposition, in preclinical stage remains
challenging, as less significant morphological changes can be observed. As a
result, few AD classification models are generalizable to the brain amyloid
positivity classification task. Blood-based biomarkers (BBBMs), on the other
hand, have recently achieved remarkable success in predicting brain amyloid
positivity and identifying individuals with high risk of being brain amyloid
positive. However, individuals in medium risk group still require gold standard
tests such as Amyloid PET for further evaluation. Inspired by the recent
success of transformer architectures, we propose a geometric deep learning
model based on transformer that is both scalable and robust to variations in
input volumetric mesh size. Our work introduced a novel tokenization scheme for
tetrahedral meshes, incorporating anatomical landmarks generated by a
pre-trained Gaussian process model. Our model achieved superior classification
performance in AD classification task. In addition, we showed that the model
was also generalizable to the brain amyloid positivity prediction with
individuals in the medium risk class, where BM alone cannot achieve a clear
classification. Our work may enrich geometric deep learning research and
improve AD diagnosis accuracy without using expensive and invasive PET scans.
