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Identification of pulmonary fissures using a piecewise plane fitting algorithm.

Suicheng Gu1, David Wilson, Zhimin Wang

  • 1Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, United States.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|July 4, 2012
PubMed
Summary

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This study presents an automated method for identifying pulmonary fissures in chest CT scans. The novel approach accurately detects fissures with high precision, offering improved efficiency for clinical applications.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Radiology

Background:

  • Pulmonary fissures are crucial anatomical landmarks in chest CT.
  • Accurate identification of fissures aids in diagnosing various lung conditions.
  • Manual fissure identification is time-consuming and prone to inter-observer variability.

Purpose of the Study:

  • To develop and validate an automated computerized scheme for identifying pulmonary fissures in 3D chest CT images.
  • To assess the accuracy, robustness, and efficiency of the proposed method compared to a reference standard and a previous approach.

Main Methods:

  • A novel approach using plane fitting in small spherical lung sub-volumes to detect planar patches representing fissures.
  • Clustering of planar patches based on spatial coherence and surface area to classify fissure types (left oblique, right oblique, right horizontal).

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  • Validation against a manually created reference standard and comparison with a previously developed method on 30 chest CT examinations.

    • Main Results:

    • The automated scheme achieved an average discrepancy of approximately 1.0mm and a maximum discrepancy of 20.5mm compared to the reference standard.
    • 94% of identified fissure voxels were within 3mm of the reference standard.
    • The new scheme demonstrated smaller discrepancies and improved efficiency (approx. 8 minutes per CT scan) compared to a previous method.

    Conclusions:

    • The developed automated scheme accurately and robustly identifies pulmonary fissures in chest CT scans.
    • The method offers significant improvements in computational efficiency over existing approaches.
    • This automated tool shows promise for enhancing diagnostic workflows in thoracic imaging.