Automatic Left and Right Lung Separation Using Free-Formed Surface Fitting on Volumetric CT

Abstract

This study presents a completely automated method for separating the left and right lungs using free-formed surface fitting on volumetric computed tomography (CT). The left and right lungs are roughly divided using iterative 3-dimensional morphological operator and a Hessian matrix analysis. A point set traversing between the initial left and right lungs is then detected with a Euclidean distance transform to determine the optimal separating surface, which is then modeled from the point set using a free-formed surfacefitting algorithm. Subsequently, the left and right lung volumes are smoothly and directly separated using the separating surface. The performance of the proposed method was estimated by comparison with that of a human expert on 44 CT examinations. For all data sets, averages of the root mean square surface distance, maximum surface distance, and volumetric overlap error between the results of the automatic and the manual methods were 0.032 mm, 2.418 mm, and 0.017 %, respectively. Our study showed the feasibility of automatically separating the left and right lungs by identifying the 3D continuous separating surface on volumetric chest CT images.

Fully Automated Lung Lobe Segmentation in Volumetric Chest CT with 3D U-Net: Validation with Intra- and Extra-Datasets

“…Second, the segmented lungs were split into the left and right lungs. When the left and right lungs were weakly connected, they were easily separated using a small number of iterations based on 3D morphological operations, 3D distance transform, and surface fitting algorithms [17,18]. To check if the lungs are…”

“…Finally, the separated left/right lungs were resized to 160×160×128 in order to optimize the data for GPU architectures. In our method, lung segmentation and half-lung separation of our previous study were performed to obtain rough coordinates and size of square-cropped each lung image [17].…”

A Visually Guided Framework for Lung Segmentation and Visualization in Chest CT Images

“…When viewed on sectional CT slices, the anterior junctions between the left and right lungs may be very thin with high magnitude of gradient, as shown in Figs.2a. In many cases, gray-scale thresholding fails to separate the left and right lungs near these junctions [1,3], as shown in Figs.2b. Therefore, we joined in gradient attributes on the basis of the gray to construct the 2D feature space.…”

“…Hence, a fast and intuitive lung segmentation and visualization method could have remarkable practical significance and clinical value for optimize screening efficiency. Thus far, many lung segmentation methods have been studied by researchers, see [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] and references therein. Here, we classify them simply into two classes: the automatic segmentation methods and the interactive segmentation methods.…”

A method for avoiding overlap of left and right lungs in shape model guided segmentation of lungs in CT volumes

“…Park et al 8 specified the ROI based on a pair of consecutive CT images and "guide" the dynamic programming algorithm by selecting the start and end points of the search in an adaptive manner. Recently, Lee et al 9 proposed a 3D approach based on iterative morphological operations and an Euclidean distance transform to determine a separating surface. If a predefined maximum number of 3D morphological erosion operations is not successful in splitting left and right lungs, the approach utilizes a fallback method where information from a Hessian matrix analysis is incorporated into the erosion process.…”