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Computer Science > Computer Vision and Pattern Recognition

arXiv:2109.00532 (cs)
[Submitted on 1 Sep 2021 (v1), last revised 23 Sep 2021 (this version, v2)]

Title:TransforMesh: A Transformer Network for Longitudinal modeling of Anatomical Meshes

Authors:Ignacio Sarasua, Sebastian Pölsterl, Christian Wachinger
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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 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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2109.00532 [cs.CV]
  (or arXiv:2109.00532v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2109.00532

Submission history

From: Ignacio Sarasua [view email]
[v1] Wed, 1 Sep 2021 14:21:58 UTC (1,946 KB)
[v2] Thu, 23 Sep 2021 13:54:23 UTC (1,946 KB)
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