Computerized lung nodule detection: comparison of performance for low-dose and standard-dose helical CT scans

The vast amount of image data acquired during a computed tomography (CT) scan makes lung nodule detection a burdensome task. Moreover, the growing acceptance of low-dose CT for lung cancer screening promises to further impact radiologists' workloads. Therefore, we have developed a computerized method to automatically analyze structures within a CT scan and identify those structures that represent lung nodules. Gray-level thresholding is performed to segment the lungs in each section to produce a segmented lung volume, which is then iteratively thresholded. At each iteration, remaining voxels are grouped into contiguous three-dimensional structures. Structures that satisfy a volume criterion then become nodule candidates. The set of nodule candidates is subjected to feature analysis. To distinguish candidates representing nodule and non-nodule structures, a rule-based approach is combined with an automated classifier. This method was applied to 43 standard-dose (diagnostic) CT scans and 13 low-dose CT scans. The method achieved an overall detection sensitivity of 71% with 1.5 false-positive detections per section on the standard-dose database and 71% sensitivity with 1.2 false-positive detections per section on the low-dose database. This automated method demonstrates promising performance in its ability to accurately detect lung nodules in standard-dose and low-dose CT images.