title: Pulmonary lobe segmentation in CT examinations using implicit surface fitting.
abstract: Lobe identification in computed tomography (CT) examinations is often an important consideration during the diagnostic process as well as during treatment planning because of their relative independence of each other in terms of anatomy and function. In this paper, we present a new automated scheme for segmenting lung lobes depicted on 3-D CT examinations. The unique characteristic of this scheme is the representation of fissures in the form of implicit functions using Radial Basis Functions (RBFs), capable of seamlessly interpolating "holes" in the detected fissures and smoothly extrapolating the fissure surfaces to the lung boundaries resulting in a "natural" segmentation of lung lobes. A previously developed statistically based approach is used to detect pulmonary fissures and the constraint points for implicit surface fitting are selected from detected fissure surfaces in a greedy manner to improve fitting efficiency. In a preliminary assessment study, lobe segmentation results of 65 chest CT examinations, five of which were reconstructed with three section thicknesses of 0.625 mm, 1.25 mm, and 2.5 mm, were subjectively and independently evaluated by two experienced chest radiologists using a five category rating scale (i.e., excellent, good, fair, poor, and unacceptable). Thirty-three of 65 examinations (50.8%) with a section thickness of 0.625 mm were rated as either "excellent" or "good" by both radiologists and only one case (1.5%) was rated by both radiologists as "poor" or "unacceptable." Comparable performance was obtained with a slice thickness of 1.25 mm, but substantial performance deterioration occurred in examinations with a section thickness of 2.5 mm. The advantages of this scheme are its full automation, relative insensitivity to fissure completeness, and ease of implementation.
authors.affiliation: Imaging Research Division, Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA. puj@upmc.edu
authors.forename: Jiantao
authors.initials: J
authors.lastname: Pu
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
authors.forename: Joseph K
authors.initials: JK
authors.lastname: Leader
authors.forename: Carl
authors.initials: C
authors.lastname: Fuhrman
authors.forename: Friedrich
authors.initials: F
authors.lastname: Knollmann
authors.forename: Amy
authors.initials: A
authors.lastname: Klym
authors.forename: David
authors.initials: D
authors.lastname: Gur
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*