title: Automatic delineation of the diaphragm in computed tomographic images.
abstract: Segmentation of the internal organs in medical images is a difficult task. By incorporating a priori information regarding specific organs of interest, results of segmentation may be improved. Landmarking (i.e., identifying stable structures to aid in gaining more knowledge concerning contiguous structures) is a promising segmentation method. Specifically, segmentation of the diaphragm may help in limiting the scope of segmentation methods to the abdominal cavity; the diaphragm may also serve as a stable landmark for identifying internal organs, such as the liver, the spleen, and the heart. A method to delineate the diaphragm is proposed in the present work. The method is based upon segmentation of the lungs, identification of the lower surface of the lungs as an initial representation of the diaphragm, and the application of least-squares modeling and deformable contour models to obtain the final segmentation of the diaphragm. The proposed procedure was applied to nine X-ray computed tomographic (CT) exams of four pediatric patients with neuroblastoma. The results were evaluated against the boundaries of the diaphragm as identified independently by a radiologist. Good agreement was observed between the results of segmentation and the reference contours drawn by the radiologist, with an average mean distance to the closest point of 5.85 mm over a total of 73 CT slices including the diaphragm.
authors.affiliation: Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada. ranga@ucalgary.ca
authors.forename: Rangaraj M
authors.initials: RM
authors.lastname: Rangayyan
authors.forename: Randy H
authors.initials: RH
authors.lastname: Vu
authors.forename: Graham S
authors.initials: GS
authors.lastname: Boag
mesh_terms: D000465:Algorithms; D002675:Child, Preschool; D003196:Computer Graphics; D003964:Diaphragm / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D008297:Male; D008962:Models, Theoretical; D009447:Neuroblastoma / Q000000981:diagnostic imaging; D011856:Radiographic Image Enhancement / Q000295:instrumentation / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*