A Computational geometry approach to automated pulmonary fissure segmentation in CT examinations.
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 </= 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 +/-0.92 to 2.04 +/-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.
Pu
Jiantao
J
Imaging Research Division, Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA. puj@upmc.edu
Leader
Joseph K
JK
Zheng
Bin
B
Knollmann
Friedrich
F
Fuhrman
Carl
C
Sciurba
Frank C
FC
Gur
David
D
D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging; D008171:Lung Diseases / Q000175:diagnosis / Q000000981:diagnostic imaging; D008954:Models, Biological; D016011:Normal Distribution; D014057:Tomography, X-Ray Computed