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Esophagus segmentation from 3D CT data using skeleton prior-based graph cut.

Damien Grosgeorge1, Caroline Petitjean, Bernard Dubray

  • 1Université de Rouen, LITIS EA 4108, 22 Boulevard Gambetta, 76183 Rouen Cedex, France.

Computational and Mathematical Methods in Medicine
|September 27, 2013
PubMed
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This study introduces a novel method for segmenting the esophagus in CT scans using a skeleton-shape model. The technique improves accuracy for radiotherapy planning by addressing low-contrast challenges in medical imaging.

Area of Science:

  • Medical imaging analysis
  • Radiotherapy planning
  • Computational anatomy

Background:

  • Accurate segmentation of organs at risk is crucial for effective radiotherapy.
  • Esophagus segmentation in CT scans is challenging due to low tissue contrast.
  • Existing methods may struggle with the low contrast and complex anatomy of the esophagus.

Purpose of the Study:

  • To develop and evaluate an original method for 3D esophagus segmentation in thoracic CT scans.
  • To improve the accuracy and reliability of esophagus segmentation for radiotherapy treatment planning.
  • To address the challenge of low contrast in CT images for muscle tissue segmentation.

Main Methods:

  • A novel approach utilizing a skeleton-shape model to guide segmentation.

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  • The method involves two main steps: 3D segmentation using graph cut with skeleton prior, followed by 2D propagation.
  • Application of the method to thoracic CT scans for 3D esophagus segmentation.

    • Main Results:

    • The proposed method demonstrates encouraging results in segmenting the 3D esophagus.
    • Successful application of the skeleton-shape model to guide graph cut segmentation.
    • Validation of the 2D propagation step for refining segmentation accuracy.

    Conclusions:

    • The developed method shows promise for accurate esophagus segmentation in CT images.
    • The skeleton-shape model effectively guides segmentation in challenging low-contrast scenarios.
    • This technique offers a valuable tool for enhancing radiotherapy treatment planning accuracy.