AI-based estimation of volumetric bone mineral density versus classical QCT image processing for the prediction of vertebral fracture risk (#257)
Nicolai R Krekiehn
1
,
Eren B Yilmaz
1
2
,
Timo Damm
1
,
Oluwabukunmi M Akinloye
3
,
Eric S Orwoll
4
,
Sandra Freitag-Wolf
3
,
Claus-C. Glüer
1
- i²Lab@Section Biomedical Imaging, Dept. of Radiology and Neuroradiology, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Schleswig-Holstein, Germany
- Dept. of Computer Science, Ostfalia University of Applied Sciences, Wolfenbüttel, Germany
- Institute of Medical Informatics and Statistics, Kiel University, University Hospital Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Schleswig-Holstein, Germany
- Division of Endocrinology, Diabetes and Clinical Nutrition, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA
Publish consent withheld
- Krekiehn, N.R., Yilmaz, E.B., Kruse, H.C., Meyer, C., Glüer, C.C. (2023). Automated Deep-learning-based Vertebral Body Localization and Instance Segmentation for Osteoporosis Assessment using CT. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_37