  The longitudinal modeling of neuroanatomical changes related to Alzheimer's
disease (AD) is crucial for studying the progression of the disease. To this
end, we introduce TransforMesh, a spatio-temporal network based on transformers
that models longitudinal shape changes on 3D anatomical meshes. While
transformer and mesh networks have recently shown impressive performances in
natural language processing and computer vision, their application to medical
image analysis has been very limited. To the best of our knowledge, this is the
first work that combines transformer and mesh networks. Our results show that
TransforMesh can model shape trajectories better than other baseline
architectures that do not capture temporal dependencies. Moreover, we also
explore the capabilities of TransforMesh in detecting structural anomalies of
the hippocampus in patients developing AD.
