Abstract
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 thatmodels 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.
Publication
Machine Learning in Medical Imaging