Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach
Malte Maria Sieren(Universitäres Kinderwunschzentrum Lübeck), Alex Frydrychowicz(University Hospital Schleswig-Holstein), Thekla Helene Oecherting(University Hospital Schleswig-Holstein), Marco Horn(University Hospital Schleswig-Holstein), Nick Weiss(Fraunhofer Institute for Digital Medicine), Erik Stahlberg(University Hospital Schleswig-Holstein), Jörg Barkhausen(University Hospital Schleswig-Holstein), Franz Wegner(University Hospital Schleswig-Holstein), Cornelia Widmann(University Hospital Schleswig-Holstein), Florian Link(Fraunhofer Institute for Digital Medicine), Jan Peter Goltz(Sana Klinikum), Jan Hendrik Moltz(Fraunhofer Institute for Digital Medicine)
Cited by 38
Related Papers
Time‐resolved 3D MR velocity mapping at 3T: Improved navigator‐gated assessment of vascular anatomy and blood flow
|Journal of Magnetic Resonance Imaging|2007|379
Evaluation of 3D blood flow patterns and wall shear stress in the normal and dilated thoracic aorta using flow-sensitive 4D CMR
|Journal of Cardiovascular Magnetic Resonance|2012|233
Three‐dimensional analysis of segmental wall shear stress in the aorta by flow‐sensitive four‐dimensional‐MRI
|Journal of Magnetic Resonance Imaging|2009|178
Risk of cancer incidence before the age of 15 years after exposure to ionising radiation from computed tomography: results from a German cohort study
|Radiation and Environmental Biophysics|2015|178
Initial experience with 64-slice cardiac CT: non-invasive visualization of coronary artery bypass grafts
|European Heart Journal|2006|156