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Updated: Sep 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Automatic contour quality assurance using deep-learning based contours.

Barbara Marquez1,2, David Fuentes2,3, Christine B Peterson2,4

  • 1Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America.

Physics in Medicine and Biology
|June 18, 2025
PubMed
Summary
This summary is machine-generated.

Integrating dose metrics with geometric comparisons significantly enhances automated quality assurance for auto-contouring models in radiation therapy. This approach improves the detection of clinically relevant errors in organ-at-risk segmentation.

Keywords:
artificial intelligenceauto-contouringcervicaldeep learninghead and neckpeer reviewquality assurance

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Artificial Intelligence in Medicine

Background:

  • Automated quality assurance (QA) is crucial for safe auto-contouring in radiation therapy.
  • Geometric comparisons alone between auto-contouring models are insufficient for detecting clinically significant errors.
  • Integrating dose metrics can potentially improve the accuracy of automated QA systems.

Purpose of the Study:

  • To investigate if including dose metrics enhances a two-contour QA system for auto-contouring models.
  • To assess the effectiveness of combined geometric and dosimetric comparisons in identifying auto-contouring errors.
  • To improve the reliability of automated QA for radiation therapy planning.

Main Methods:

  • Generated volumetric modulated arc therapy plans for 86 head and neck (H&N) and 50 cervical (GYN) cancer patients.
  • Compared auto-contoured organs-at-risk (OARs) with manually delineated OARs to identify dosimetric errors (Dmean or Dmax ≥ 2 Gy).
  • Utilized a second auto-contouring model for verification and compared it with the primary model using geometric and dosimetric metrics; logistic regression predicted errors.

Main Results:

  • Including dose metrics in logistic regression improved prediction of mean dose errors for small H&N structures.
  • Dose metrics enhanced the prediction of maximum dose errors for small and medium H&N structures, and for GYN structures.
  • The combined approach showed improved performance in detecting clinically significant auto-contouring errors.

Conclusions:

  • Combining geometric and dosimetric comparisons in a two-contour QA system significantly improves the detection of auto-contouring errors.
  • This enhanced QA approach is vital for the safe and effective deployment of auto-contouring models in clinical practice.
  • Further research can refine these methods for broader application in radiation therapy planning.