A computerized scheme for lung nodule detection in multiprojection chest radiography.
A computerized scheme for lung nodule detection in multiprojection chest radiography.
复制标题
多投影胸部放射线摄影中肺结节检测的计算机化方案。
DOI：
10.1118/1.3694096
复制
发表时间：
2012
期刊：
影响因子：
3.8
通讯作者：
Samei,Ehsan
中科院分区：
文献类型：
--
作者：
Guo,Wei;Li,Qiang;Boyce,SarahJ;McAdams,HPage;Shiraishi,Junji;Doi,Kunio;Samei,Ehsan
目的：我们之前的研究表明，在临床实践中，多投影胸片可以显著提高放射科医生对肺结节的检测能力。在这项研究中，作者进一步验证了多投影胸片摄影可以大大提高计算机辅助诊断（CAD）方案的性能。方法：我们的数据库包括59例患者，其中43例有45例结节，16例无结节。45个结节中，真实结节7个，模拟结节38个。作者开发了一种传统的CAD方案和一种新的融合CAD方案来检测肺结节。传统的CAD方案包括四个步骤：(1)肺内初始候选结节的识别，(2)基于动态规划的候选结节分割，(3)从候选结节中提取33个特征，(4)使用分段线性分类器减少假阳性。传统的CAD方案对被测者的三幅投影图像分别进行独立处理，并丢弃三幅图像之间的相关信息。融合CAD方案包括传统CAD方案的四个步骤和两个额外的步骤，即(5)对一个主题的三幅图像中的所有候选对象进行配准，(6)对三幅图像中注册的候选对象之间的相关信息进行整合。整合步骤保留在受试者的三幅图像中检测到至少两次的所有候选图像，并将在三幅图像中仅检测到一次的候选图像作为假阳性去除。采用“留一受试者”测试方法对两种CAD方案的性能水平进行评估。结果：在70%、65%和60%的灵敏度下，我们的传统CAD方案分别报告每张图像14.7、11.3和8.6个假阳性，而我们的融合CAD方案报告每张图像3.9、1.9和1.2个假阳性，每位患者分别报告5.5、2.8和1.7个假阳性。传统CAD方案的低性能可能归因于胸片的高噪声水平，以及大多数结节的小尺寸和低对比度。结论：本研究表明，在多投影胸片中融合相关信息可以显著提高CAD方案对肺结节的检测效果。
Purpose:Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer‐aided diagnostic (CAD) scheme.Methods: Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification of initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave‐one‐subject‐out testing method was used for evaluation of the performance levels of the two CAD schemes.Results: At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules.Conclusions: This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.