A computational geometry approach to automated pulmonary fissure segmentation in CT examinations

Jiantao Pu, Joseph K. Leader, Bin Zheng, Friedrich D Knollmann, Carl Fuhrman, Frank C. Sciurba, David Gur

Research output: Contribution to journalArticlepeer-review

85 Scopus citations

Abstract

Identification of pulmonary fissures, which form the boundaries between the lobes in the lungs, may be useful during clinical interpretation of computed tomography (CT) examinations to assess the early presence and characterization of manifestation of several lung diseases. Motivated by the unique nature of the surface shape of pulmonary fissures in 3-D space, we developed a new automated scheme using computational geometry methods to detect and segment fissures depicted on CT images. After a geometric modeling of the lung volume using the marching cubes algorithm, Laplacian smoothing is applied iteratively to enhance pulmonary fissures by depressing nonfissure structures while smoothing the surfaces of lung fissures. Next, an extended Gaussian image based procedure is used to locate the fissures in a statistical manner that approximates the fissures using a set of plane patches. This approach has several advantages such as independence of anatomic knowledge of the lung structure except the surface shape of fissures, limited sensitivity to other lung structures, and ease of implementation. The scheme performance was evaluated by two experienced thoracic radiologists using a set of 100 images (slices) randomly selected from 10 screening CT examinations. In this preliminary evaluation 98.7% and 94.9% of scheme segmented fissure voxels are within 2 mm of the fissures marked independently by two radiologists in the testing image dataset. Using the scheme detected fissures as reference, 89.4% and 90.1% of manually marked fissure points have distance ${\leq}2$ mm to the reference suggesting a possible under-segmentation of the scheme. The case-based root mean square (rms) distances (errors) between our scheme and the radiologist ranged from $1.48 \pm 0.92$ to $2.04 \pm 3.88$ mm. The discrepancy of fissure detection results between the automated scheme and either radiologist is smaller in this dataset than the interreader variability.

Original languageEnglish (US)
Article number4703240
Pages (from-to)710-719
Number of pages10
JournalIEEE Transactions on Medical Imaging
Volume28
Issue number5
DOIs
StatePublished - May 2009
Externally publishedYes

Keywords

  • Computer-aided detection (CAD)
  • Extended Gaussian image (EGI)
  • Pulmonary fissure
  • Segmentation
  • Shape analysis

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software

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