Evaluation of computerized detection of pulmonary embolism in independent data sets of computed tomographic pulmonary angiographic (CTPA) scans

Computed tomographic pulmonary angiography (CTPA) has been reported to be an effective means for clinical diagnosis of pulmonary embolism (PE). We are developing a computer-aided diagnosis (CAD) system for assisting radiologists in detection of pulmonary embolism in CTPA images. The pulmonary vessel tree is extracted based on the analysis of eigenvalues of Hessian matrices at multiple scales followed by 3D hierarchical EM segmentation. A multiprescreening method is designed to identify suspicious PEs along the extracted vessels. A linear discriminant analysis (LDA) classifier with feature selection is then used to reduce false positives (FPs). Two data sets of 59 and 69 CTPA PE cases were randomly selected from patient files at the University of Michigan (UM) and the PIOPED II study, respectively, and used as independent training and test sets. The PEs that were identified by three experienced thoracic radiologists were used as the gold standard. The detection performance of the CAD system was assessed by free response receiver operating characteristic analysis. The results indicated that our PE detection system can achieve a sensitivity of 80% at 18.9 FPs/case on the PIOPED cases when the LDA classifier was trained with the UM cases. The test sensitivity with the UM cases is 80% at 22.6 FPs/cases when the LDA classifier was trained with the PIOPED cases.