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.

Fig. 1

Examples of VDE images in which ribs and clavicles were suppressed by using our MTANN technique. (a) Original CXR. (b) VDE soft-tissue image. Arrows indicate nodules.

Fig. 2

Main diagram for our CADe scheme with the VDE technology based on MTANNs.

Fig. 3

Our CADe scheme for detection of lung nodules in CXRS by use of our VDE technology based on MTANNs.

Fig. 4

Illustration of changes in rib contrast in VDE soft-tissue images and the VDE bone image used to create those soft-tissue images. (a) Original image. (b) VDE soft-tissue image with 20% rib contrast. (c) 40% rib contrast. (d) 70% rib contrast. (e) 90% rib contrast. (f) VDE bone image.

Fig. 5

Effect of the change in rib contrast on the sensitivity of our CADe scheme.

Fig. 6

Improvement in detection of nodule candidates by use of our VDE technology. (a) Nodule candidate detection results based on original images. (b) Nodule candidate detection results based on VDE images. Arrows indicate nodules. The nodules overlapping ribs that had been missed with the original images were detected with the VDE images.

Fig. 7

Illustration of the nodules detected in the nodule candidate detection step: classified as FP by use of the features based on original image, but classified as TP by use of the features based on both the original and VDE images. (a) Nodules with segmentation results in original image. (b) Nodules with segmentation results in VDE images.

Fig. 8

FROC curves indicating the improvement in the performance of our CADe schemes with SVM classifier by use of our VDE technology for the JSRT database. Error bars indicate 95% confidence intervals.

Fig. 9

Illustration of detection of nodules by our VDE-based CADe scheme (indicated by circles). (a) TP (arrow) and FPs of the original CADe scheme. (b) False negative (arrow) and FP of the VDE-based CADe scheme.

Fig. 10

FROC curves indicating the performance of the VDE-based CADe scheme by nodule subtlety for the JSRT database.

Fig. 11

FROC curves indicating the performance of the VDE-based CADe scheme by nodule size for the JSRT database.

Fig. 12

FROC curves indicating the performance of the VDE-based CADe scheme by pathology for the JSRT database.

Fig. 13

Illustration of the improvement in nodule detection with our VDE technology. CADe marks are indicated by circles. (a) False negatives (arrow) and FPs of the original CADe scheme. (b) TPs (arrow) and FPs of the VDE-based CADe scheme with the VDE technology.

Fig. 14

Illustration of the improvement in specificity by our VDE technology. CADe marks are indicated by circles. (a) TPs (arrow) and FPs of the original CADe scheme. (b) TPs (arrow) and FPs of the VDE-based CADe scheme with the VDE technology.