A quality assurance framework for routine monitoring of deep learning cardiac substructure computed tomography segmentation models in radiotherapy.

Journal: Medical physics
Published Date: Nov 28, 2023

Abstract

BACKGROUND: For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor-collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance of these models by a process called domain shift. Tools to routinely monitor and predict segmentation performance are needed for quality assurance. Here, we develop an approach to perform such monitoring and performance prediction for cardiac substructure segmentation.

Authors

  • Xiyao Jin
    Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, China.
  • Yao Hao
    Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA.
  • Jessica Hilliard
    Department of Radiation Oncology, Washington University in St Louis School of Medicine, St. Louis, Missouri, USA.
  • Zhehao Zhang
    Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Maria A Thomas
    Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
  • Hua Li
    Department of Stomatology, The First Medical Center Chinese PLA General Hospital Beijing China.
  • Abhinav K Jha
    Department of Biomedical Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, United States of America.
  • Geoffrey D Hugo
    Department of Radiation Oncology, Washington University School of Medicine, St. Louis, 63110, MO, USA.

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