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Three-dimensional lung tumor segmentation from x-ray computed tomography using sparse field active models.

Joseph Awad1, Amir Owrangi, Lauren Villemaire

  • 1Robarts Research Institute, London, Ontario, Canada. jawad@imaging.robarts.ca

Medical Physics
|February 11, 2012
PubMed
Summary
This summary is machine-generated.

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This study presents an automated tool for lung tumor segmentation on CT scans, significantly reducing measurement time and observer variability. The algorithm shows high correlation and reproducibility compared to manual methods, improving efficiency in radiology workflows.

Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Pathology

Background:

  • Manual segmentation of lung tumors is time-consuming and prone to observer variability.
  • Accurate tumor measurement is crucial for radiology and radiation oncology workflows.
  • Automated tools can enhance reproducibility and efficiency in clinical practice.

Purpose of the Study:

  • To develop an automated lung tumor segmentation tool for pulmonary metastatic tumors using CT images.
  • To improve the reproducibility and decrease the time required for tumor boundary segmentation.
  • To provide a reliable alternative to manual measurement in clinical settings.

Main Methods:

  • Developed an automated segmentation algorithm using shape constrained Otsu multithresholding (SCOMT) and sparse field active surface (SFAS).

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  • The algorithm requires the observer to select only the tumor center for initialization.
  • Volumetric image analysis of chest CT images was performed for algorithm validation.

    • Main Results:

    • The automated algorithm demonstrated significant correlations with manual measurements in 1D, 2D, and 3D.
    • High intra-observer reproducibility was observed for both algorithm and manual measurements (ICC > 0.98).
    • The algorithm exhibited lower intra-observer coefficient of variation (CV%) compared to manual segmentation across all measurement dimensions.

    Conclusions:

    • An automated segmentation algorithm was successfully developed for pulmonary metastatic tumors.
    • The algorithm requires minimal operator input (single seed point selection).
    • The automated tool significantly reduces intra-observer variability and measurement time, correlating well with manual methods.