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Proceedings Paper

Automatic seed point identification and main artery segmentation for pulmonary vascular tree segmentation and tracking in computed tomographic pulmonary angiography (CTPA)
Author(s): Yanhui Guo; Chuan Zhou; Heang-Ping Chan; Jean W. Kuriakose; Aamer Chughtai; Jun Wei; Lubomir M. Hadjiiski; Ella A. Kazerooni
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Paper Abstract

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.

Paper Details

Date Published: 23 February 2012
PDF: 6 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152P (23 February 2012); doi: 10.1117/12.912848
Show Author Affiliations
Yanhui Guo, Univ. of Michigan (United States)
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)
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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