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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.
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