Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.

Abstract

Authors

• Malte Maria Sieren
  Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany. malte.sieren@uksh.de.
• Cornelia Widmann
  Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
• Nick Weiss
  Fraunhofer MEVIS, Am Fallturm 1, 28359, Bremen, Germany. Electronic address: nick.weiss@mevis.fraunhofer.de.
• Jan Hendrik Moltz
  Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany.
• Florian Link
  Fraunhofer Institute for Digital Medicine MEVIS, Lübeck/Bremen, Germany.
• Franz Wegner
  Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
• Erik Stahlberg
  Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
• Marco Horn
  Department for Vascular Surgery, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.
• Thekla Helene Oecherting
  Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
• Jan Peter Goltz
  Institute for Diagnostic and Interventional Radiology/Neuroradiology, Sana Clinic, Lübeck, Germany.
• Joerg Barkhausen
  Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
• Alex Frydrychowicz
  Department of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.

Keywords

External Resources