Contour subregion error detection methodology using deep learning auto-segmentation.

Journal: Medical physics
Published Date: Nov 1, 2023

Abstract

BACKGROUND: Inaccurate manual organ delineation is one of the high-risk failure modes in radiation treatment. Numerous automated contour quality assurance (QA) systems have been developed to assess contour acceptability; however, manual inspection of flagged cases is a time-consuming and challenging process, and can lead to users overlooking the exact error location.

Authors

  • Jingwei Duan
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • Mark E Bernard
  • Yi Rong
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.
  • James R Castle
    Carina Medical LLC, Lexington, Kentucky, USA.
  • Xue Feng
    Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.
  • Jeremiah D Johnson
    Department of Orthopaedic Surgery, Regions Hospital, St. Paul, Minnesota; Department of Orthopaedic Surgery, University of Minnesota, Minneapolis, Minnesota.
  • Quan Chen
    Management School, Zhongshan Institute, University of Electronic Science and Technology of China, Guangdong, 528402, China.

Keywords

External Resources

Popular Topics
Recent Journals