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Computerized Detection of Lung Nodules by Means of Virtual Dual-Energy Radiography

机译:通过虚拟双能射线照相术语计算机化检测肺结节

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摘要

Major challenges in current computer-aided detection (CADe) schemes for nodule detection in chest radiographs (CXRs) are to detect nodules that overlap with ribs and/or clavicles and to reduce the frequent false positives (FPs) caused by ribs. Detection of such nodules by a CADe scheme is very important, because radiologists are likely to miss such subtle nodules. Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of “virtual dual-energy” (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs). To reduce rib-induced FPs and detect nodules overlapping with ribs, we incorporated the VDE technology in our CADe scheme. The VDE technology suppressed rib and clavicle opacities in CXRs while maintaining soft-tissue opacity by use of the MTANN technique that had been trained with real dual-energy imaging. Our scheme detected nodule candidates on VDE images by use of a morphologic filtering technique. Sixty morphologic and gray-level-based features were extracted from each candidate from both original and VDE CXRs. A nonlinear support vector classifier was employed for classification of the nodule candidates. A publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs was used for testing our CADe scheme. All nodules were confirmed by computed tomography examinations, and the average size of the nodules was 17.8 mm. Thirty percent (42/140) of the nodules were rated “extremely subtle” or “very subtle” by a radiologist. The original scheme without VDE technology achieved a sensitivity of 78.6% (110/140) with 5 (1165/233) FPs per image. By use of the VDE technology, more nodules overlapping with ribs or clavicles were detected and the sensitivity was improved substantially to 85.0% (119/140) at the same FP rate in a leave-one-out cross-validation test, whereas the FP rate was reduced to 2.5 (583/233) per image at the same sensitivity level as the original CADe scheme obtained (Difference between the specificities of the original and the VDE-based CADe schemes was statistically significant). In particular, the sensitivity of our VDE-based CADe scheme for subtle nodules (66.7% = 28/42) was statistically significantly higher than that of the original CADe scheme (57.1% = 24/42). Therefore, by use of VDE technology, the sensitivity and specificity of our CADe scheme for detection of nodules, especially subtle nodules, in CXRs were improved substantially.
机译:当前用于胸部X光片(CXR)中的结节检测的计算机辅助检测(CADe)方案的主要挑战是检测与肋骨和/或锁骨重叠的结节,并减少由肋骨引起的频繁的假阳性(FP)。通过CADe方案检测此类结核非常重要,因为放射科医生很可能会错过这些细微的结核。我们在这项研究中的目的是通过使用“虚拟双能”(VDE)CXR开发具有更高灵敏度和特异性的CADe方案,其中通过大规模训练的人工神经网络(MTANN)抑制肋骨和锁骨。为了减少肋骨引起的FP并检测与肋骨重叠的结节,我们将VDE技术纳入了我们的CADe方案。 VDE技术通过使用经过真正双能成像训练的MTANN技术,抑制了CXR中的肋骨和锁骨混浊,同时保持了软组织的不透明性。我们的方案通过使用形态学过滤技术在VDE图像上检测到了结节候选。从原始和VDE CXR的每个候选中提取了60个基于形态学和基于灰度的特征。使用非线性支持向量分类器对候选结节进行分类。一个公开的数据库包含140个CXR和93个正常CXR中的140个结节,用于测试我们的CADe方案。所有结节均通过计算机断层扫描检查确认,结节的平均大小为17.8毫米。放射科医生将百分之三十(42/140)的结核定为“极细微”或“极细微”。没有VDE技术的原始方案每个图像有5个(1165/233)FP的灵敏度达到了78.6%(110/140)。通过使用VDE技术,在留一法交叉验证测试中,在相同的FP率下,检测到更多的结节与肋骨或锁骨重叠的结节,并且灵敏度显着提高到85.0%(119/140)。在与原始CADe方案相同的敏感度水平下,每幅图像的成像速率降低到2.5(583/233)(原始方案和基于VDE的CADe方案的特异性之间的差异具有统计学意义)。特别是,基于VDE的CADe方案对细微结节的敏感性(66.7%= 28/42)在统计学上显着高于原始CADe方案的敏感性(57.1%= 24/42)。因此,通过使用VDE技术,我们的CADe方案在CXR中检测结节(尤其是细小结节)的灵敏度和特异性得到了显着提高。

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