SparseMeshCNN with Self-Attention for Segmentation of Large Meshes

Bjørn Hansen, Mathias Lowes, Thomas Ørkild, Anders Dahl, Vedrana Dahl, Ole De Backer, Oscar Camara, Rasmus Paulsen, Christian Ingwersen, Kristine Sørensen

Abstract: In many clinical applications, 3D mesh models of human anatomies are important tools for visualization, diagnosis, and treatment planning. Such 3D mesh models often have a high number of vertices to capture the complex shape, and processing these large meshes on readily available graphic cards can be a challenging task. To accommodate this, we present a sparse version of MeshCNN called SparseMeshCNN, which can process meshes with more than 60 000 edges. We further show that adding non-local attention in the network can mitigate the small receptive field and improve the results. The developed methodology was applied to separate the Left Atrial Appendage (LAA) from the Left Atrium (LA) on 3D mesh models constructed from medical images, but the method is general and can be put to use in any application within mesh classification or segmentation where memory can be a concern.

External IDs:doi:10.7557/18.6281