A key issue in current computer-aided diagnostic(CAD) schemes for nodule detection in CT is the large number of false positives, because current schemes use only global three-dimensional (3D) information to detect nodules and discard useful local two-dimensional (2D) information. Thus, the authors integrated local and global information to markedly improve the performance levels of CAD schemes.Methods:
Our database was obtained from the standard CTlung nodule database created by the LungImageDatabase Consortium (LIDC). It consisted of 85 CT scans with 111 nodules of 3 mm or larger in diameter. The 111 nodules were confirmed by at least two of the four radiologists participated in the LIDC. Twenty-six nodules were missed by two of the four radiologists and were thus very difficult to detect. The authors developed five CAD schemes for nodule detection in CT using global 3D information (3D scheme), local 2D information (2D scheme), and both local and global information (2D + 3D scheme, 2D − 3D scheme, and 3D − 2D scheme). The 3D scheme, which was developed previously, used only global 3D information and discarded local 2D information, as other CAD schemes did. The 2D scheme used a uniform viewpoint reformation technique to decompose a 3D nodule candidate into a set of 2D reformatted images generated from representative viewpoints, and selected and used “effective” 2D reformatted images to remove false positives. The 2D + 3D scheme, 2D − 3D scheme, and 3D − 2D scheme used complementary local and global information in different ways to further improve the performance of lung nodule detection. The authors employed a leave-one-scan-out testing method for evaluation of the performance levels of the five CAD schemes.Results:
At the sensitivities of 85%, 80%, and 75%, the existing 3D scheme reported 17.3, 7.4, and 2.8 false positives per scan, respectively; the 2D scheme improved the detection performance and reduced the numbers of false positives to 7.6, 2.5, and 1.3 per scan; the 2D + 3D scheme further reduced those to 2.7, 1.9, and 0.6 per scan; the 2D − 3D scheme reduced those to 7.6, 2.1, and 0.8 per scan; and the 3D − 2D scheme reduced those to 17.3, 1.6, and 1.0 per scan.Conclusions:
The local 2D information appears to be more useful than the global 3D information for nodule detection, particularly, when it is integrated with 3D information.
This work was supported by USPHS Grant No. R01 CA113870, and W.G. is supported in part by China Natural Science Foundation grant 60972117. CAD technologies developed by Qiang Li and his colleagues have been licensed to companies including Hologic, Inc., Riverain Medical Group, Median Technology, Mitsubishi Space Software Co., General Electric Corporation, and Toshiba Corporation. It is the policy of Duke University that investigators disclose publicly actual or potential significant financial interests that may appear to be affected by research activities.
III.A. Identification of initial nodule candidates
III.B. 3D feature determination
III.C. 2D features determination
III.C.1. Reconstruction of the 2D reformatted images
III.C.2. Nodule segmentation in the 2D reformatted images
III.C.3. 2D feature determination
III.D. Feature selection and classification
III.E. Evaluation methodologies
IV.A. Effect of the number of viewpoints on the performance of nodule detection
IV.B. Effect of the percentage threshold on the performance of nodule detection
IV.C. Effect of the rules on the performance of nodule detection and needed time
IV.D. Comparison of the performance levels with different reference standards
IV.E. Comparison of the performance levels for different schemes
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