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Med Phys. 2009 Aug;36(8):3385-96.

Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): performance evaluation with independent data sets.

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Department of Radiology, University of Michigan, Med Inn Building C479, 1500 E. Medical Center Drive, Ann Arbor, Michigan 48109, USA.


The authors are developing a computer-aided detection system for pulmonary emboli (PE) in computed tomographic pulmonary angiography (CTPA) scans. The pulmonary vessel tree is extracted using a 3D expectation-maximization segmentation method based on the analysis of eigen-values of Hessian matrices at multiple scales. A parallel multiprescreening method is applied to the segmented vessels to identify volume of interests (VOIs) that contained suspicious PE. A linear discriminant analysis (LDA) classifier with feature selection is designed to reduce false positives (FPs). Features that characterize the contrast, gray level, and size of PE are extracted as input predictor variables to the LDA classifier. With the IRB approval, 59 CTPA PE cases were collected retrospectively from the patient files (UM cases). With access permission, 69 CTPA PE cases were randomly selected from the data set of the prospective investigation of pulmonary embolism diagnosis (PIOPED) II clinical trial. Extensive lung parenchymal or pleural diseases were present in 22/59 UM and 26/69 PIOPED cases. Experienced thoracic radiologists manually marked 595 and 800 PE as the reference standards in the UM and PIOPED data sets, respectively. PE occlusion of arteries ranged from 5% to 100%, with PE located from the main pulmonary artery to the subsegmental artery levels. Of the 595 PE identified in the UM cases, 245 and 350 PE were located in the subsegmental arteries and the more proximal arteries, respectively. The detection performance was assessed by free response ROC (FROC) analysis. The FROC analysis indicated that the PE detection system could achieve an overall sensitivity of 80% at 18.9 FPs/case for the PIOPED cases when the LDA classifier was trained with the UM cases. The test sensitivity with the UM cases was 80% at 22.6 FPs/cases when the LDA classifier was trained with the PIOPED cases. The detection performance depended on the arterial level where the PE was located and on the percentage of occlusion. The sensitivity was lower for PE in the subsegmental arteries than in more proximal arteries and was lower for PE with less than 20% occlusion. The results indicate that the PE detection system achieves high sensitivity for PE detection on independent CTPA scans for both the PIOPED and UM data sets and demonstrate the potential that the automated PE detection approach can be generalized to unknown cases.

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