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17 March 2006 Computerized lung nodule detection on screening CT scans: performance on juxta-pleural and internal nodules
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We are developing a computer-aided detection (CAD) system for lung nodules in thoracic CT volumes. Our CAD system includes an adaptive 3D pre-screening algorithm to segment suspicious objects, and a false-positive (FP) reduction stage to classify the segmented objects as true nodules or normal lung structures. We found that the effectiveness of the FP reduction stage was limited by the different characteristics of the objects in the internal and the juxta-pleural (JP) regions. The purpose of this study was to evaluate object characteristics in the internal and JP regions of a lung CT scan, and to develop different FP reduction classifiers for JP and internal objects. Our FP reduction technique utilized shape, grayscale, and gradient features, as well as the scores of a newly-developed neural network trained on the eigenvalues of the Hessian matrix in a volume of interest containing the suspicious object. We designed an algorithm to automatically label the objects as internal or JP. Based on a training set of 75 CT scans containing internal and JP nodules, two FP classifiers were trained separately for objects in the two types of lung regions. The system performance was evaluated on an independent test set of 27 low dose screening scans. An experienced chest radiologist identified 64 solid nodules (mean diameter: 5.3 mm, range: 3.0-12.9 mm) on the test cases, of which 33 were internal and 31 were JP. Our adaptive 3D prescreening algorithm detected 28 internal and 29 JP nodules. At 80% sensitivity, the average number of FPs was 3.9 and 9.7 in the internal and JP regions per scan, respectively. In comparison, a classifier designed to work on both types of nodules had an average of 29.4 FPs per scan at the same sensitivity. Our results indicate that it is more effective to use two different classifiers for JP and internal nodules because of their different characteristics. FPs in the JP region were more difficult to distinguish from true nodules. Further investigation of task-specific FP reduction techniques is needed.
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Berkman Sahiner, Lubomir M. Hadjiiski, Heang-Ping Chan, Chuan Zhou, and Jun Wei "Computerized lung nodule detection on screening CT scans: performance on juxta-pleural and internal nodules", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61445S (17 March 2006);

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