MedmeshCNN - Enabling meshcnn for medical surface models.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. It outperformed state-of-the-art methods in classification and segmentation tasks of popular benchmarking datasets. The medical domain provides a large amount of complex 3D surface models that may benefit from processing with MeshCNN. However, several limitations prevent outstanding performances on highly diverse medical surface models. Within this work, we propose MedMeshCNN as an expansion dedicated to complex, diverse, and fine-grained medical data.

Authors

  • Lisa Schneider
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany.
  • Annika Niemann
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany. Electronic address: annika.niemann@ovgu.de.
  • Oliver Beuing
    Department for Radiology, AMEOS Hospital Bernburg, Germany.
  • Bernhard Preim
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.
  • Sylvia Saalfeld
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.