A Machine- (Deep-) Learning Approach to Fully Automated Regional Bone, Cartilage, and Phenotype Analysis in Knee Osteoarthritis –Development, Validation, Application and Qualification

Projekt: Forschung

Status	Abgeschlossen
Tatsächlicher Beginn/ -es Ende	1/10/18 → 1/01/20

Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep-Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative.
Eckstein, F. (Erstautor/-in), Chaudhari, A. S., Fuerst, D. (Co-Autor/-in), Gaisberger, M. (Co-Autor/-in), Kemnitz, J. (Co-Autor/-in), Baumgartner, C. F., Konukoglu, E., Hunter, D. J. & Wirth, W. (Letztautor/-in), 2022, in: ARTHRITIS CARE & RESEARCH. 74, 6, S. 929-936
Publikation: Beitrag in Fachzeitschrift › Originalarbeit › Begutachtung

26 Quellenangaben (Web of Science)
Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.
Wirth, W. (Erstautor/-in), Eckstein, F. (Co-Autor/-in), Kemnitz, J. (Co-Autor/-in), Baumgartner, C. F., Konukoglu, E., Fuerst, D. (Co-Autor/-in) & Chaudhari, A. S., 2021, in: MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE. 34, 3, S. 337-354
Publikation: Beitrag in Fachzeitschrift › Originalarbeit › Begutachtung

37 Quellenangaben (Web of Science)

Projektdetails