The effect of nodule segmentation on the accuracy of computerized lung nodule detection on CT scans: comparison on a data set annotated by multiple radiologists

Abstract

In computerized nodule detection systems on CT scans, many features that are useful for classifying whether a nodule candidate identified by prescreening is a true positive depend on the shape of the segmented object. We designed two segmentation algorithms for detailed delineation of the boundaries for nodule candidates. The first segmentation technique was a three-dimensional (3D) region-growing (RG) method which grew the object across multiple CT sections. The second technique was based on a 3D active contour (AC) model. A training set of 94 CT scans was used for algorithm design. An independent set of 62 scans, each read by multiple radiologists, was used for testing. Thirty-three scans were collected from patient files at the University of Michigan and 29 scans by the Lung Imaging Database Consortium (LIDC). In this study, we concentrated on the detection of internal lung nodules having a size ≥3 mm that were not pure ground-glass opacities. Of the lesions marked by one or multiple radiologists, 124 nodules satisfied these criteria and were considered true nodules. The performance of the detection system in the AC feature space, RG feature space, and the combined feature space were compared using free-response receiver operating curves (FROC). The FROC curve using the combined feature space was significantly higher than that using the RG feature space or the AC feature space alone (p=0.02 and 0.03, respectively). At a sensitivity of 70% for internal non-GGO nodules, the FP rates were 2.2, 2.2, and 1.5 per scan, respectively, for the RG, AC, and the combined methods. Our results indicate that the 3D AC algorithm can provide useful features to improve nodule detection on CT scans.