Trade-off of different deep learning-based auto-segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI.

Journal: Medical physics
Published Date: Apr 1, 2025

Abstract

BACKGROUND: As auto-segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. One notable example is the use of adult-trained commercial software for the contouring of organs at risk (OARs) of pediatric patients.

Authors

  • Alana Thibodeau-Antonacci
    Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada.
  • Marija Popović
    Department of Computing, Imperial College London, South Kensington, London SW7 2AZ, UK. Email: m.popovic@imperial.ac.uk.
  • Ozgur Ates
    Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America.
  • Chia-Ho Hua
    Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States of America.
  • James Schneider
    Department of Radiation Oncology, Jewish General Hospital, Montreal, Canada.
  • Sonia Skamene
    Division of Radiation Oncology, McGill University Health Centre, Montreal, QC, Canada.
  • Carolyn Freeman
    Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada.
  • Shirin Abbasinejad Enger
    Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada.
  • James Man Git Tsui
    Department of Radiation Oncology, McGill University Health Center, Montreal, Québec, Canada.

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