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Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining

Ying Zhang1, Asma Amjad2, Jie Ding3

  • 1Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas.

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|September 5, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel contour quality classification (CQC) method to evaluate auto-segmented contours for deep learning-based auto-segmentation (DLAS). The CQC method accurately assesses contour quality, improving clinical usability and addressing limitations of current metrics.

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Radiology and radiation oncology

Background:

  • Current metrics for auto-segmented contour quality have limitations in reflecting clinical usefulness.
  • There is a need for improved methods to evaluate the quality of contours generated by deep learning-based auto-segmentation (DLAS).

Purpose of the Study:

  • To develop a novel contour quality classification (CQC) method for clinical usability-oriented evaluation of DLAS.
  • To combine multiple quantitative metrics into a single classification system.

Main Methods:

  • Developed a CQC method using supervised ensemble tree classification models with 7 quantitative metrics.
  • Trained organ-specific models for 5 abdominal organs using MRI data.
  • Validated models on independent MRI and CT datasets, comparing with interobserver variation (IOV) and a threshold-based approach.

Main Results:

  • Achieved high performance with average AUC of 0.982 ± 0.01 (MRI) and 0.979 ± 0.01 (CT).
  • Demonstrated high mean accuracy (95.8% ± 1.7% for MRI, 94.3% ± 2.1% for CT) and low risk rate.
  • CQC results closely matched IOV and significantly outperformed the threshold-based method.

Conclusions:

  • The CQC models exhibit high performance in classifying contour slice quality.
  • This method provides an intuitive and comprehensive solution for clinical evaluation of DLAS.
  • The CQC addresses limitations of existing metrics, enhancing clinical utility.