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A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in

Robert Poel1, Elias Rüfenacht2, Stefan Scheib3

  • 1Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Bern, Switzerland. robert.poel@insel.ch.

Communications Medicine
|July 31, 2025
PubMed
Summary

A new framework validates automatic segmentation in radiotherapy, showing it is faster and accurate. This method ensures reliable clinical implementation of deep learning tools for organs at risk contouring.

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Area of Science:

  • Radiotherapy and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Medical Physics

Background:

  • Manual organ at risk contouring in radiotherapy is a lengthy process, taking 1-4 hours per patient.
  • Deep learning-based automatic segmentation offers a potential solution but requires robust validation.
  • Current evaluation methods for automatic segmentation lack standardization and comprehensive assessment.

Purpose of the Study:

  • To introduce a Comprehensive Multifaceted Technical Evaluation framework for validating automatic segmentation models in radiotherapy.
  • To assess the geometric accuracy, clinical acceptability, time efficiency, and dosimetric impact of an automatic segmentation model.

Main Methods:

  • Developed a framework integrating quantitative geometric, qualitative expert, time efficiency, and dosimetric evaluations.
  • Applied the framework to an in-house automatic segmentation model for brain organs at risk.
  • Evaluated the model using data from 100 cases and feedback from 8 radiation oncology experts across 4 institutions.

Main Results:

  • The automatic segmentation model achieved a geometric accuracy of 0.78, surpassing manual inter-rater variability.
  • 88% of automatically segmented structures were deemed clinically acceptable by experts, requiring minor adjustments.
  • The evaluation and adjustment process took an average of 22 minutes, significantly less than the 69 minutes for manual contouring.
  • Dosimetric analysis revealed minimal impact on treatment plans, with small average dose differences.

Conclusions:

  • The Comprehensive Multifaceted Technical Evaluation framework offers a rigorous method for validating automatic segmentation tools.
  • Standardized benchmarks and community consensus are crucial for the clinical implementation and comparative analysis of segmentation models.