Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning.

Journal: Practical radiation oncology
Published Date: Sep 2, 2024

Abstract

PURPOSE: The current commonly used metrics for evaluating the quality of auto-segmented contours have limitations and do not always reflect the clinical usefulness of the contours. This work aims to develop a novel contour quality classification (CQC) method by combining multiple quantitative metrics for clinical usability-oriented contour quality evaluation for deep learning-based auto-segmentation (DLAS).

Authors

  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Asma Amjad
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  • Jie Ding
    State Key Laboratory of Respiratory Disease, Joint School of Life Sciences, Guangzhou Chest Hospital, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, China.
  • Christina Sarosiek
    Department of Radiation Oncology, Medical College of Wisconsin, United States of America.
  • Mohammad Zarenia
    Department of Radiation Oncology, Medical College of Wisconsin, United States of America.
  • Renae Conlin
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • William A Hall
    Department of Radiation Oncology, Medical College of Wisconsin and Clement J. Zablocki VA Medical Center, Milwaukee, Wisconsin. Electronic address: whall@mcw.edu.
  • Beth Erickson
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.
  • Eric Paulson
    Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin.

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