Federated learning for musculoskeletal segmentation in CT
This collaboration explored federated learning for musculoskeletal segmentation in CT imaging across institutions in Germany and Japan.
Abstract
This collaboration explored federated learning for musculoskeletal segmentation in CT imaging across institutions in Germany and Japan. Using labeled CT data from NAIST and an unlabeled whole-body CT cohort from University Hospital Bonn, we evaluated cross-site generalization and domain transfer under realistic scanner and population shifts. In parallel, a secure federated learning infrastructure was deployed and ethical approval obtained at University Hospital Bonn. While the project did not progress to publication-ready federated training due to annotation constraints and deployment complexity, it provided valuable methodological and organizational insights.
Perspective
The collaboration strengthened institutional expertise in federated learning under real-world clinical constraints and informed subsequent national and international projects with more harmonized data availability.
