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Current status and future potential of computer-aided diagnosis in medical imaging

Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, 5841 South Maryland, MC 2026, Chicago, IL 60637, USA

View larger version (166K): [in a new window]   Figure 1. Illustration of the computer output marked by two arrows; one indicates the correct detection of a subtle nodule in the left lung, and the other corresponds to a false positive, which is a normal structure in the mediastinum.

View larger version (35K): [in a new window]   Figure 2. Az values without and with computer-aided diagnosis (CAD) for 16 radiologists in the detection of lung nodules on chest radiographs. 60 normals and 60 abnormals with lung nodules of varying subtlety were used.

View larger version (111K): [in a new window]   Figure 3. Illustration of malignant and benign nodules on chest radiographs together with the likelihood measure of malignancy obtained with a computer-aided diagnosis (CAD) scheme by use of linear discriminant analysis (LDA) and on artificial neural network (ANN). A computer output above or below 0.50 indicates the likelihood of malignancy or benignancy, respectively.

View larger version (27K): [in a new window]   Figure 4. Receiver operating characteristic (ROC) curves for distinction between malignant and benign nodules, on chest radiographs without and with the computer-aided diagnosis (CAD) outputs such as those shown in Figure 3. 16 radiologists participated in an observer study in the interpretation of 53 chest radiographs, including 31 primary lung cancers and 22 benign nodules.

View larger version (34K): [in a new window]   Figure 5. Illustration of the basic structure of a massive training artificial neural network (MTANN) together with image data as input and teacher image data as output, used for training the MTANN. Typically, 10 nodules and 10 false positives are used for training a single MTANN.

View larger version (18K): [in a new window]   Figure 6. Illustration of the usefulness of the massive training artificial neural network (MTANN) in reducing the number of false positives (FPs) in computerised detection of nodules in low dose CT (LDCT) images. Single MTANN or multi-MTANN was applied to a rule-based computer-aided diagnosis (CAD) scheme for 63 LDCT scans with 71 nodules including 66 primary cancers and 5 benign nodules.

View larger version (95K): [in a new window]   Figure 7. Illustration of subtle missed cancers, which were detected correctly by our computer-aided diagnosis (CAD) scheme, on low dose CT images obtained from a lung cancer screening.

View larger version (22K): [in a new window]   Figure 8. Receiver operating characteristic (ROC) curves without and with computer-aided diagnosis (CAD) output for detection of lung cancers in low dose CT images. Six radiologists participated in an observer study, detecting missed peripheral lung cancers in 27 cases including 17 CT scans with cancer and 10 CT scans without cancer.

View larger version (81K): [in a new window]   Figure 9. Illustration of malignant and benign nodules with pure ground-glass opacity (GGO), mixed GGO and solid opacity. These nodules were segmented for subsequent analysis for determination of the likelihood measure of malignancy.

View larger version (26K): [in a new window]   Figure 10. Receiver operating characteristic (ROC) curves without and with computer-aided diagnosis (CAD) output for distinction between malignant and benign nodules on high resolution CT. The images used in this study included 28 primary lung cancers (6–20 mm) and 28 begin nodules that were selected by matching their size and pattern to the cancers. A ROC curve for the computer results is also shown.

View larger version (83K): [in a new window]   Figure 11. Illustration of the potential usefulness of similar images for distinction between malignant and benign lesions. The image in the centre is an unknown case on low dose CT, and two sets of benign and malignant nodules that would be similar to the unknown case are shown on the left and right, respectively.

View larger version (131K): [in a new window]   Figure 12. Illustration of "gold standard" for one normal and six abnormal patterns of diffuse lung diseases on high resolution CT images that were determined by the areas marked independently by three radiologists.

View larger version (62K): [in a new window]   Figure 13. Illustration of images (96 x 96) selected from the seven slices in Figure 12, histograms of region-of-interest (ROI) images, and output images for air density components, line components, nodular components and multilocular components.

View larger version (17K): [in a new window]   Figure 14. Illustration of the distribution of mean CT values and the standard deviation of CT values for six abnormal patterns and normals on high resolution CT.

View larger version (81K): [in a new window]   Figure 15. Illustration of an original magnetic resonance angiography (MRA) image and three images selectively enhanced for dot, line, and plane objects, all of which were produced by maximum intensity projection (MIP) image processing. Circles indicate a large (7.5 mm) aneurysm.

View larger version (50K): [in a new window]   Figure 16. An original magnetic resonance angiography (MRA) image and an enlarged aneurysm detected by computer.