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Uncertainty-aware refinement framework for ovarian tumor segmentation in CECT volume.

Jiaqi Hu1, Zhiming Cui2, Xiao Zhang2,3

  • 1Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Department of Gynecologic Oncology, Women's Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Medical Physics
|October 20, 2023
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Summary

This study introduces an uncertainty-aware framework for precise ovarian tumor segmentation in CECT images, significantly improving accuracy and identifying small tumors effectively for better radiotherapy planning.

Keywords:
ovarian cancerrefinementtumor segmentationuncertainty estimation

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Computational Pathology

Background:

  • Ovarian cancer is a lethal gynecological disease.
  • Accurate segmentation of ovarian tumors in contrast-enhanced computed tomography (CECT) images is vital for radiotherapy planning.
  • Automated segmentation faces challenges like inhomogeneous backgrounds and ambiguous boundaries, leading to high model uncertainty.

Purpose of the Study:

  • To propose an uncertainty-aware refinement framework for accurate ovarian tumor segmentation in CECT images.
  • To estimate and refine regions with high predictive uncertainty.
  • To improve segmentation accuracy for better clinical decision-making.

Main Methods:

  • Utilized an approximate Bayesian network to detect coarse regions of interest (ROIs) for tumors and uncertain areas.
  • Employed a subsequent segmentation network that narrows the search area and prioritizes uncertain regions.
  • Integrated two guidance modules learning implicit functions to map uncertain features to organ or boundary manifolds.

Main Results:

  • Achieved high performance on internal testing data (77 cases): Dice 86.31%, Jaccard 73.93%, HD95 15.17 mm, ASSD 2.57 mm.
  • Demonstrated superior performance over state-of-the-art models, especially for small tumors (Dice >20% higher for volumes <20 cm³).
  • Validated robust performance on external data (38 cases): Dice 83.74%, Jaccard 69.55%, HD95 12.31 mm, ASSD 2.32 mm.

Conclusions:

  • The proposed framework significantly outperforms existing methods in ovarian tumor segmentation.
  • The method reduces under/over-segmentation and improves identification of small tumors.
  • The framework shows strong potential for clinical application in radiotherapy.