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Proceedings Paper

Automated detection of pulmonary embolism (PE) in computed tomographic pulmonary angiographic (CTPA) images: multiscale hierachical expectation-maximization segmentation of vessels and PEs
Author(s): Chuan Zhou; Heang-Ping Chan; Lubomir M. Hadjiiski; Aamer Chughtai; Smita Patel; Philip N. Cascade; Berkman Sahiner; Jun Wei; Jun Ge; Ella A. Kazerooni
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Paper Abstract

CT pulmonary angiography (CTPA) has been reported to be an effective means for clinical diagnosis of pulmonary embolism (PE). We are developing a computer-aided detection (CAD) system to assist radiologist in PE detection in CTPA images. 3D multiscale filters in combination with a newly designed response function derived from the eigenvalues of Hessian matrices is used to enhance vascular structures including the vessel bifurcations and suppress non-vessel structures such as the lymphoid tissues surrounding the vessels. A hierarchical EM estimation is then used to segment the vessels by extracting the high response voxels at each scale. The segmented vessels are pre-screened for suspicious PE areas using a second adaptive multiscale EM estimation. A rule-based false positive (FP) reduction method was designed to identify the true PEs based on the features of PE and vessels. 43 CTPA scans were used as an independent test set to evaluate the performance of PE detection. Experienced chest radiologists identified the PE locations which were used as "gold standard". 435 PEs were identified in the artery branches, of which 172 and 263 were subsegmental and proximal to the subsegmental, respectively. The computer-detected volume was considered true positive (TP) when it overlapped with 10% or more of the gold standard PE volume. Our preliminary test results show that, at an average of 33 and 24 FPs/case, the sensitivities of our PE detection method were 81% and 78%, respectively, for proximal PEs, and 79% and 73%, respectively, for subsegmental PEs. The study demonstrates the feasibility that the automated method can identify PE accurately on CTPA images. Further study is underway to improve the sensitivity and reduce the FPs.

Paper Details

Date Published: 30 March 2007
PDF: 8 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65142F (30 March 2007); doi: 10.1117/12.713769
Show Author Affiliations
Chuan Zhou, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Aamer Chughtai, Univ. of Michigan (United States)
Smita Patel, Univ. of Michigan (United States)
Philip N. Cascade, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Jun Ge, Univ. of Michigan (United States)
Ella A. Kazerooni, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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