Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation.

Journal: Medical physics
Published Date: Feb 25, 2023

Abstract

BACKGROUND: Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics.

Authors

  • Jingwei Duan
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • Mark E Bernard
  • James R Castle
    Carina Medical LLC, Lexington, Kentucky, USA.
  • Xue Feng
    Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia.
  • Chi Wang
    School of Computer Science & Technology, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 100081, China.
  • Mark C Kenamond
    Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.
  • Quan Chen
    Management School, Zhongshan Institute, University of Electronic Science and Technology of China, Guangdong, 528402, China.

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