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.

Anatomy-based algorithm for automatic segmentation of human diaphragm in noncontrast computed tomography images

“…This overestimation results in a different diaphragm shape compared to its real shape, which can affect the accuracy of a respective diaphragm's biomechanical model. In addition, the results obtained by our group are more accurate than those reported by Rangayyan et al 14 (MDCP ¼ 2.55 mm versus MDCP ¼ 5.85 mm). It is noteworthy that their segmentation goal is different from our segmentation goal and what they have achieved satisfies their goal, which is abdominal tumor segmentation.…”

“…The results presented by Rangayyan et al 14 are closer to what is required for diaphragm biomechanical modeling. However, as Rangayyan et al…”

“…Another algorithm was proposed by Zhou et al, 13 in which they modeled the diaphragm as a thin plate spline, which passes through the bottom surface of the lungs. A similar approach to that of Zhou et al 13 was proposed by Rangayyan et al, 14 in which they modeled the diaphragm domes as quadratic surfaces. The results obtained by this group are more accurate as they used active contours 15 to pull the initial approximate diaphragm surface toward its actual surface in the image.…”

“…While applications such as biomechanical modeling require the entire diaphragm as well as accurate diaphragm boundary conditions, the only group that attempted to segment the entire diaphragm was Rangayyan et al 14 However, as Rangayyan et al 14 stated in their discussion, their algorithm overestimates the lumber part of the diaphragm, and the details of diaphragm attachments to the ribcage and spine are missing in the final results.…”

Development of an Automated 3D Segmentation Program for Volume Quantification of Body Fat Distribution Using CT

“…Next, to eliminate fat around the heart, an automated method is used to extract the diaphragm surface by deforming a thin-plate model to match the bottom surface of the lung. [13][14][15] The outer body contour for each slice was extracted using a simple threshold method together with morphological filtering. To extract the subcutaneous layer and visceral fat area on each CT slice, radii from two detected points are drawn at fixed 5-degree increments towards the outer contour in all slices except those in the pelvis Fig.…”