Search

Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately under suboptimal conditions, such as vessels occluded by PEs, surrounded by lymphoid tissues or lung diseases, and crossing with other vessels. In this study, we developed a new vessel refinement method utilizing curved planar reformation (CPR) technique combined with optimal path finding method (MHES-CROP). The MHES segmented vessels straightened in the CPR volume was refined using adaptive gray level thresholding where the local threshold was obtained from least-square estimation of a spline curve fitted to the gray levels of the vessel along the straightened volume. An optimal path finding method based on Dijkstra's algorithm was finally used to trace the correct path for the vessel of interest. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOIs) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 32.9±10.2% using the MHES method to 9.9±7.9% using the MHES-CROP method. The accuracy of vessel segmentation was improved significantly (p<0.05). The intraclass correlation coefficient (ICC) of the segmented vessel volume between the automated segmentation and the reference standard was improved from 0.919 to 0.988. Quantitative comparison of the MHES method and the MHES-CROP method with the reference standard was also evaluated by the Bland-Altman plot. This preliminary study indicates that the MHES-CROP method has the potential to improve PE detection.

We are developing a computer-aided detection (CAD) system to assist radiologists in pulmonary embolism (PE) detection in computed tomographic pulmonary angiography (CTPA). Automatic segmentation and tracking of pulmonary vessels is a fundamental step to define the search space for PE detection. For automated tracking of pulmonary arteries, it is important to accurately identify the seed points to track the left and right pulmonary vessel trees. In this study, we developed an automatic seed point identification and pulmonary main artery (PMA) segmentation method. The seed point was derived from the bifurcation region where the pulmonary trunk artery splits into the left and right. A 3D recursive optimal path finding method (RPF) was developed to find the paths from the bifurcation point to the end of the left and right PMAs. The PMAs were finally extracted along the PMA paths using morphological operation. Two and 18 CTPA cases was used for training and testing, respectively. A set of points in the central luminal space of the PMA were manually marked as the "reference standard" by two experienced chest radiologists using a computer interface. A total of 3870 were marked in the test set. A voxel located on the computer-identified paths of the PMA was counted as a true PMA voxel when its distance to the closest reference standard point is within a threshold. Our results show that 95.6% (17681/18502) and 88.8% (16439/18502) of computer identified PMA path points were within a distance of 10 mm and 8 mm to the closest reference point, respectively, and 100% (18/18) of the seed points were detected in the bifurcation region. 2.7% (104/3870) of the reference standard points were not contained in the computer segmented vessels and counted as false negative points.

Vessel segmentation is a fundamental step in an automated pulmonary embolism (PE) detection system. The purpose of this study is to improve the segmentation scheme for pulmonary vessels affected by PE and other lung diseases. We have developed a multiscale hierarchical vessel enhancement and segmentation (MHES) method for pulmonary vessel tree extraction based on the analysis of eigenvalues of Hessian matrices. However, it is difficult to segment the pulmonary vessels accurately when the vessel is occluded by PEs and/or surrounded by lymphoid tissues or lung diseases. In this study, we developed a method that combines MHES with level set refinement (MHES-LSR) to improve vessel segmentation accuracy. The level set was designed to propagate the initial object contours to the regions with relatively high gray-level, high gradient, and high compactness as measured by the smoothness of the curvature along vessel boundaries. Two and eight CTPA scans were randomly selected as training and test data sets, respectively. Forty volumes of interest (VOI) containing "representative" vessels were manually segmented by a radiologist experienced in CTPA interpretation and used as reference standard. The results show that, for the 32 test VOIs, the average percentage volume error relative to the reference standard was improved from 31.7±10.9% using the MHES method to 7.7±4.7% using the MHES-LSR method. The correlation between the computer-segmented vessel volume and the reference standard was improved from 0.954 to 0.986. The accuracy of vessel segmentation was improved significantly (p<0.05). The MHES-LSR method may have the potential to improve PE detection.