RootPainter3D: Interactive-machine-learning enables rapid and accurate contouring for radiotherapy.

Journal: Medical physics
Published Date: Dec 10, 2021

Abstract

PURPOSE: Organ-at-risk contouring is still a bottleneck in radiotherapy, with many deep learning methods falling short of promised results when evaluated on clinical data. We investigate the accuracy and time-savings resulting from the use of an interactive-machine-learning method for an organ-at-risk contouring task.

Authors

  • Abraham George Smith
    Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
  • Jens Petersen
    Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Cynthia Terrones-Campos
    Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
  • Anne Kiil Berthelsen
    Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
  • Nora Jarrett Forbes
    Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
  • Sune Darkner
    Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
  • Lena Specht
    Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
  • Ivan Richter Vogelius
    Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.

Keywords

External Resources

Popular Topics
Recent Journals