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Proceedings Paper

Pulmonary vessel segmentation utilizing curved planar reformation and optimal path finding (CROP) in computed tomographic pulmonary angiography (CTPA) for CAD applications
Author(s): Chuan Zhou; Heang-Ping Chan; Jean W. Kuriakose; Aamer Chughtai; Jun Wei; Lubomir M. Hadjiiski; Yanhui Guo; Smita Patel; Ella A. Kazerooni
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Paper Abstract

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.

Paper Details

Date Published: 23 February 2012
PDF: 9 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150N (23 February 2012); doi: 10.1117/12.912446
Show Author Affiliations
Chuan Zhou, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Jean W. Kuriakose, Univ. of Michigan (United States)
Aamer Chughtai, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Yanhui Guo, Univ. of Michigan (United States)
Smita Patel, Univ. of Michigan (United States)
Ella A. Kazerooni, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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