High performance lung nodule detection schemes in CT using local and global information.
High performance lung nodule detection schemes in CT using local and global information.
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DOI：
10.1118/1.4737109
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发表时间：
2012-08
期刊：
影响因子：
3.8
通讯作者：
Wei Guo;Qiang Li
中科院分区：
文献类型：
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作者：
Wei Guo;Qiang Li
当前用于CT中结节检测的计算机辅助诊断（CAD）方案中的关键问题是大量的假阳性，因为当前方案仅使用全局三维（3D）信息来检测结节并且丢弃有用的局部二维（2D）信息。因此，作者将局部和全局信息相结合，以显着提高CAD方案的性能水平。方法：我们的数据库是从肺图像数据库联盟（LIDC）创建的标准CT肺结节数据库中获得的。其中CT扫描85次，直径≥ 3 mm结节111个。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。四名放射科医生中有两名漏诊了二十六个结节，因此很难发现。作者开发了5种CAD方案，用于使用全局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）进行CT结节检测。与其他CAD方案一样，以前开发的3D方案仅使用全局3D信息并丢弃局部2D信息。2D方案使用统一的视点重组技术将3D结节候选者分解为从代表性视点生成的一组2D重组图像，并选择和使用“有效”2D重组图像以去除假阳性。2D + 3D方案、2D - 3D方案和3D - 2D方案以不同的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。作者采用了留一扫描测试方法来评估五个CAD方案的性能水平。结果在85%、80%和75%的灵敏度下，现有的3D方案报告的每次扫描的假阳性数分别为17.3、7.4和2.8，而2D方案提高了检测性能，将每次扫描的假阳性数减少到7.6、2.5和1.3; 2D + 3D方案进一步将它们减少到每次扫描2.7、1.9和0.6; 2D - 3D方案将它们减少到每次扫描7.6、2.1和0.8; 3D - 2D方案将它们减少到每次扫描17.3、1.6和1.0。结论：局部二维信息比全局三维信息更有利于结节的检测，尤其是当局部二维信息与三维信息结合时。
PURPOSE 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 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. 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.