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Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

Rationale and Objectives

We have been developing a computer-aided diagnostic (CAD) scheme for lung nodule detection in order to assist radiologists in the detection of lung cancer in thin-section computed tomography (CT) images.

Materials and Methods

Our database consisted of 117 thin-section CT scans with 153 nodules, obtained from a lung cancer screening program at a Japanese university (85 scans, 91 nodules) and from clinical work at an American university (32 scans, 62 nodules). The database included nodules of different sizes (4–28 mm, mean 10.2 mm), shapes, and patterns (solid and ground-glass opacity (GGO)). Our CAD scheme consisted of modules for lung segmentation, selective nodule enhancement, initial nodule detection, feature extraction, and classification. The selective nodule enhancement filter was a key technique for significant enhancement of nodules and suppression of normal anatomic structures such as blood vessels, which are the main sources of false positives. Use of an automated rule-based classifier for reduction of false positives was another key technique; it resulted in a minimized overtraining effect and an improved classification performance. We used a case-based four-fold cross-validation testing method for evaluation of the performance levels of our computerized detection scheme.

Results

Our CAD scheme achieved an overall sensitivity of 86% (small: 76%, medium-sized: 94%, large: 95%; solid: 86%, mixed GGO: 89%, pure GGO: 81%) with 6.6 false positives per scan; an overall sensitivity of 81% (small: 69%, medium-sized: 91%, large: 91%; solid: 79%, mixed GGO: 88%, pure GGO: 81%) with 3.3 false positives per scan; and an overall sensitivity of 75% (small: 60%, medium-sized: 88%, large: 87%; solid: 70%, mixed GGO: 87%, pure GGO: 81%) with 1.6 false positives per scan.

Conclusion

The experimental results indicate that our CAD scheme with its two key techniques can achieve a relatively high performance for nodules presenting large variations in size, shape, and pattern.

Lung cancer is the leading cause of deaths among all types of cancer in the United States ( ). The number of deaths it causes is greater than the total number of deaths resulting from colon cancer, breast cancer, and prostate cancer combined. Some evidence suggests that early detection of lung cancer may allow for timely therapeutic intervention, which in turn results in a more favorable prognosis for the patients. Therefore, screening programs for early detection of lung cancer have been attempted in the United States and Japan by use of computed tomography (CT) ( ). In a screening program with CT, radiologists must read a large number of images, and they are likely to overlook some lung cancers. Therefore, a computer-aided diagnostic (CAD) scheme for nodule detection ( ), which provides radiologists with the locations of nodule candidates, would be particularly useful for reduction of detection errors in the early detection of cancer in thoracic CT scans.

CAD schemes for lung nodule detection were developed first for chest radiographs ( ) and then for thick-section CT images ( ). The typical performance of current CAD schemes in thick-section CT is an 80–90% sensitivity with one or two false positives per section, which translates into tens of false positives per CT scan. The majority of false positives are caused by blood vessels and other normal anatomic structures ( ). Because of the relatively large section thickness (5–10 mm), CAD schemes for nodule detection in thick-section CT generally detect nodules on a section-by-section basis. Because most of the processing steps, such as nodule segmentation and feature extraction, are performed on two-dimensional (2D) section images, they are considered to be 2D.

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Materials and methods

Materials

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Figure 1, Distribution of nodule sizes in our database. The database contained nodules with a relatively wide range of sizes. There were 68 (44.4%) small (4–8 mm), 52 (34.0%) medium-sized (9–13 mm), and 33 (21.6%) large nodules (≥14 mm) in the database.

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Methods

Overall Scheme of Our Computerized Detection Technique

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Figure 2, Overall scheme of the computerized detection technique.

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Lung Segmentation

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Figure 3, Schemetic illustration for inclusion of a juxtapleural object.

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Image Enhancement by Use of Three Selective Filters

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Figure 4, Maximum intensity projection of ( a ) two 3D original images with nodules identified by arrows and ( b ) nodule-enhanced images.

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Initial Identification and Region Growing of Nodule Candidates

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Feature Determination

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False-Positive Reduction by Use of an Automated Rule-Based Classifier

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Evaluation of the CAD Scheme for Nodule Detection

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Results

Results of Initial Nodule Detection

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Figure 5, ( a ) Three low-contrast nodules with GGO that were successfully identified, and ( b ) the only two nodules that were missed by our initial nodule detection technique.

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Results of Final Nodule Detection

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Figure 6, Mean FROC curves for training and testing of our CAD scheme.

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Figure 7, Mean FROC curves obtained from the testing of our CAD scheme for the nodules in the American dataset, the Japanese dataset, and for all nodules.

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Figure 8, Mean FROC curves obtained from the testing of our CAD scheme for the small nodules (<9 mm), the medium-sized nodules (9–13 mm), the large nodules (>13 mm), and all nodules.

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Figure 9, Mean FROC curves obtained from the testing of our CAD scheme for the solid nodules, the mixed GGO nodules, the pure GGO nodules, and all nodules.

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Table 1

Means ± Standard Deviations of Sensitivities and False-Positive Rates per Case for Different Groups of Nodules at Four Operating Points, Obtained with Ten Trials of the Four-Fold Case-Based Cross-Validation Method. The Number Below the Title of Each Column Indicates the Number of Nodules in a Specific Nodule Group

Operating Points Sensitivities (%) for Different Groups of Nodules FP Rates per Case All (n = 153) Small (n = 68) Medium-Sized (n = 52) Large (n = 33) Solid (n = 101) Mixed GGO (n = 36) Pure GGO (n = 16) 1 75.4 ± 1.6 60.4 ± 2.3 87.5 ± 2.6 87.2 ± 3.1 70.3 ± 2.5 86.9 ± 1.3 81.3 ± 0 1.6 ± 0.2 2 81.3 ± 1.6 69.0 ± 2.4 91.3 ± 1.6 90.9 ± 2.3 78.9 ± 2.2 88.1 ± 1.3 81.3 ± 0 3.3 ± 0.3 3 86.0 ± 1.5 75.5 ± 3.0 94.0 ± 1.9 94.7 ± 2.1 85.5 ± 2.0 89.4 ± 1.2 81.3 ± 0 6.6 ± 0.4 4 92.3 ± 1.6 87.8 ± 3.7 96.2 ± 0.0 95.3 ± 1.6 93.4 ± 2.1 93.9 ± 1.2 81.3 ± 0 19.2 ± 1.7

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Discussion

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Conclusion

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Appendix

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Appendix

Optimal rule-based classifier based on linear composite features

Input: A training dataset including N nodules and a design sensitivity S to be achieved. Output: A list of rules based on composite features.

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