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

Jingwei Duan1, Mark E Bernard1, Yi Rong2

  • 1Department of Radiation Medicine, University of Kentucky, Lexington, Kentucky, USA.

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This study introduces a contour subregion error detection (CSED) system to improve radiation treatment accuracy. The CSED system effectively identifies and visualizes contour errors, enhancing quality assurance and reducing risks associated with inaccurate organ segmentation.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Medical Imaging Analysis

Background:

  • Inaccurate manual organ delineation in radiation therapy poses high-risk failure modes.
  • Current automated contour quality assurance (QA) systems require time-consuming manual inspection of flagged cases, risking overlooked errors.
  • Existing methods struggle with precise localization and visualization of subregional contour discrepancies.

Purpose of the Study:

  • To develop and validate a novel contour QA system for detecting and visualizing subregional contour errors.
  • To provide both qualitative and quantitative assessments of contour accuracy.
  • To improve the efficiency and effectiveness of radiation treatment planning QA.

Main Methods:

  • Developed a contour subregion error detection (CSED) system using surface distance discrepancies between manual and deep learning auto-segmentation (DLAS) contours.
  • Validated the system on a head and neck dataset (339 cases) using knowledge-based pass criteria from a clinical dataset (60 cases).
  • Conducted blind qualitative evaluation and re-examination by a radiation oncologist for CSED-flagged cases.

Main Results:

  • The CSED system successfully visualized diverse subregional contour errors qualitatively and quantitatively.
  • Achieved high true positive rates (0.771-0.814) and accuracies (0.730-0.759) for brainstem and parotid contours.
  • CSED-assisted review improved detection of missed errors by 75% and reduced review time significantly.

Conclusions:

  • The CSED system effectively detects, localizes, and visualizes manual segmentation errors using DLAS contours.
  • This system aids in reducing high-risk failure modes stemming from inaccurate organ segmentation in radiation therapy.
  • The CSED system enhances contour QA by providing precise error identification and visualization.