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

Abstract

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.

Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation

“…They aimed to develop a CADe scheme with improved sensitivity and specificity by the use of virtual dual-energy (VDE) CXRs where ribs and clavicles are suppressed with massive-training artificial neural networks (MTANNs) [17]. Christe et al (2013) used different computeraided detection software (CAD) at different dose levels to detect lung nodules in lung cancer screening.…”

Computerized Detection of Lesions in Diagnostic Images

“…They improved the performance by using the MTANN virtual dual-energy imaging [18]. They improved the performance substantially from the original sensitivity of 79 % with five FPs per image to a sensitivity of 85 % with the a b Figure 7.12 illustrates computer outputs from their CADe scheme without and with the MTANN virtual dual-energy imaging [18].…”

Classification and detection of lung nodules using virtual dual energy in CXR images

“…The sizes of nodules are ranges from 5 -40 mm. To facilitate comparisons of our VDE-based technology with our original scheme [1], [10], [21] and future comparisons with other methods, by use of the JSRT database [11], [16] this is publicly available. CXR images with a resolution of 2048 × 2048 pixels.…”