Comparative analysis of open-source against commercial AI-based segmentation models for online adaptive MR-guided radiotherapy.

Journal: Zeitschrift fur medizinische Physik
Published Date: May 8, 2025

Abstract

BACKGROUND AND PURPOSE: Online adaptive magnetic resonance-guided radiotherapy (MRgRT) has emerged as a state-of-the-art treatment option for multiple tumour entities, accounting for daily anatomical and tumour volume changes, thus allowing sparing of relevant organs at risk (OARs). However, the annotation of treatment-relevant anatomical structures in context of online plan adaptation remains challenging, often relying on commercial segmentation solutions due to limited availability of clinically validated alternatives. The aim of this study was to investigate whether an open-source artificial intelligence (AI) segmentation network can compete with the annotation accuracy of a commercial solution, both trained on the identical dataset, questioning the need for commercial models in clinical practice.

Authors

  • Dominik Langner
    Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany. Electronic address: dominik.langner@med.uni-tuebingen.de.
  • Marcel Nachbar
    Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
  • Monica Lo Russo
    Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
  • Simon Boeke
    Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
  • Cihan Gani
    Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
  • Maximilian Niyazi
    Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
  • Daniela Thorwarth
    Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen, a partnership between DKFZ and University Hospital Tübingen, Germany; Cluster of Excellence "Machine Learning: New Perspectives for Science", University of Tübingen, Germany.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals