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

Publikationen

!!Research output per year 2021 2021 2022 2022

• 2 Originalarbeit

!!Research output per year

!!Research output per year

Suchergebnisse

• 2022

  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)

• 2021

  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)