Guo Wei, Li Qiang
School of Computer, Shenyang Aerospace University, Daoyi Development District, Shenyang, Liaoning 110136, China.
Med Phys. 2012 Aug;39(8):5157-68. doi: 10.1118/1.4737109.
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.
Our database was obtained from the standard CT lung nodule database created by the Lung Image Database 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.
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.
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.
当前用于CT中结节检测的计算机辅助诊断（CAD）方案的一个关键问题是假阳性数量众多，因为当前方案仅使用全局三维（3D）信息来检测结节，而丢弃了有用的局部二维（2D）信息。因此，作者整合了局部和全局信息，以显著提高CAD方案的性能水平。
我们的数据库来自由肺部影像数据库联盟（LIDC）创建的标准CT肺结节数据库。它由85例CT扫描组成，有111个直径3mm或更大的结节。这111个结节由参与LIDC的四名放射科医生中的至少两名确认。四个放射科医生中有两名漏诊了26个结节，因此这些结节很难被检测到。作者开发了五种用于CT中结节检测的CAD方案，分别使用全局3D信息（3D方案）、局部2D信息（2D方案）以及局部和全局信息（2D + 3D方案、2D - 3D方案和3D - 2D方案）。之前开发的3D方案仅使用全局3D信息并丢弃局部2D信息，其他CAD方案也是如此。2D方案使用统一的视点重整技术将3D结节候选分解为由代表性视点生成的一组2D重整图像，并选择和使用“有效”的2D重整图像来去除假阳性。2D + 3D方案、2D - 3D方案和3D - 2D方案以不同方式使用互补的局部和全局信息，以进一步提高肺结节检测的性能。作者采用留一扫描法来评估这五种CAD方案的性能水平。
在敏感度为85%、80%和75%时，现有的3D方案分别报告每次扫描有17.3、7.4和2.8个假阳性；2D方案提高了检测性能，将每次扫描的假阳性数量减少到7.6、2.5和1.3个；2D + 3D方案进一步将其减少到每次扫描2.7、1.9和0.6个；2D - 3D方案将其减少到每次扫描7.6、2.1和0.8个；3D - 2D方案将其减少到每次扫描17.3、1.6和1.0个。
局部2D信息在结节检测中似乎比全局3D信息更有用，特别是当它与3D信息整合时。
