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Adaptive border marching algorithm: automatic lung segmentation on chest CT images.

Jiantao Pu1, Justus Roos, Chin A Yi

  • 1Department of Radiology, Stanford University, United States.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|June 3, 2008
PubMed
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This study introduces adaptive border marching (ABM), a novel lung segmentation algorithm for chest CT scans. ABM accurately segments lungs, including juxtapleural nodules, with minimal oversegmentation, improving diagnostic accuracy.

Area of Science:

  • Medical Imaging
  • Computational Geometry
  • Radiology

Background:

  • Lung segmentation in chest computed tomography (CT) is crucial for medical imaging analysis.
  • Accurate segmentation is challenging, particularly with diseases affecting lung borders.
  • Juxtapleural nodules and adjacent structures like the abdomen and mediastinum complicate segmentation.

Purpose of the Study:

  • To present a novel lung segmentation algorithm, adaptive border marching (ABM).
  • To demonstrate ABM's ability to reliably include juxtapleural nodules.
  • To minimize oversegmentation of adjacent anatomical regions.

Main Methods:

  • Adaptive Border Marching (ABM) algorithm utilizing geometric smoothing of lung borders.
  • Computational geometry approach applied to chest CT datasets.

Related Experiment Videos

  • Validation against expert-determined reference standards.

    • Main Results:

    • ABM successfully re-included all juxtapleural nodules in experiments.
    • Achieved average oversegmentation of 0.43% and under-segmentation of 1.63%.
    • Segmentation time under 1 minute on a standard PC, with no user interaction required post-input.

    Conclusions:

    • ABM is a robust, efficient, and straightforward lung segmentation method for chest CT.
    • The algorithm improves accuracy by including juxtapleural nodules and reducing errors.
    • Clinical impact includes potentially avoiding false negatives in CAD and enhancing volumetric accuracy.