Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with multiple aneurysms. Clinical research proposed more advanced analysis of intracranial aneurysm, but requires many complex preprocessing steps. Advanced tools for automatic aneurysm analysis are needed to transfer current research into clinical routine.

Authors

  • 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.
  • Daniel Behme
    University Clinic for Neuroradiology, Otto von Guericke University, Magdeburg, Germany.
  • Naomi Larsen
    Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein (UKSH), Kiel, 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.