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ALTIS: A fast and automatic lung and trachea CT-image segmentation method.

Azael M Sousa1, Samuel B Martins1, Alexandre X Falcão1

  • 1Laboratory of Image Data Science, Institute of Computing, University of Campinas, Campinas, Brazil.

Medical Physics
|August 23, 2019
PubMed
Summary
This summary is machine-generated.

A new method, ALTIS, accurately segments lungs and trachea in CT scans, even with abnormalities. This fast, robust approach outperforms existing techniques for analyzing lung conditions.

Keywords:
CT images of the thoraximage foresting transformmathematical morphologymedical image segmentation

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Radiology

Background:

  • Automated segmentation of lung and trachea in CT scans is crucial for medical analysis.
  • Existing methods often fail with lung abnormalities due to disease, trauma, or surgery.

Purpose of the Study:

  • To present ALTIS, a robust and fast automatic method for lung and trachea CT image segmentation.
  • To improve segmentation accuracy in the presence of diverse lung and trachea abnormalities.

Main Methods:

  • ALTIS utilizes a sequence of image foresting transforms (IFTs).
  • The method involves lung-and-trachea extraction, seed estimation, and delineation using optimum-path forests.
  • ALTIS was compared against shape model (SOSM-S, MALF) and region growing (PTK) methods.

Main Results:

  • Experiments used 1255 CT scans, including numerous anomalous cases.
  • ALTIS achieved high accuracy with an average DICE of 0.987 (lungs) and 0.898 (trachea).
  • The method demonstrated superior speed and accuracy compared to existing techniques.

Conclusions:

  • ALTIS is a highly effective and efficient method for segmenting lungs and trachea in anomalous CT scans.
  • The robust segmentation facilitates subsequent detection, segmentation, and quantitative analysis of lung abnormalities.