Computer-aided diagnosis in medical imaging: historical review, current status and future potential

Abstract

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a "second opinion" and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists' accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists.

Fig. 1

Comparison of the previous performance level marked by circles (87% sensitivity at 1.0 false positive per image) in the detection of clustered microcalcifications by computer in 1993, when the CAD technology was licensed to a company, with the estimated current performance level, marked by a gray square (98% sensitivity at 0.25 false positive per image), of the latest commercial CAD system.

Fig. 2

Illustration of a relatively large, but very subtle lung nodule (dotted circles) located in the right mediastinum region which was correctly marked by CAD (triangles) on the lateral view, but was not marked by CAD on the PA view.

Fig. 3

Illustration of the correct detection (arrowhead) by computer of a fractured vertebra (dotted circles) below the diaphragm on a lateral chest radiograph, which can be used as a second opinion. Thus, the accuracy of detection of vertebral fractures by radiologists could be improved on lateral chest radiographs, and the early diagnosis of osteoporosis could be improved.

Fig. 4

The isotropic 3D MRA image in (a) was processed by use of a selective, multi-scale enhancement filter for detection of an intracranial aneurysm (dotted circles), as illustrated in the dot-enhanced image in (b).

Fig. 5

Illustration of temporal subtraction image obtained from previous and current bone scan images. One cold lesion (white solid circle) and two hot lesions (dark dotted circles) on the subtraction image were correctly marked by computer. Thus, the temporal subtraction image for successive whole-body bone scans has the potential to enhance the interval changes between two images.

Fig. 6

Illustration of subtle, difficult nodules in HRCT. The correct computer output for the likelihood of malignancy was able to assist radiologists in improving their decisions, as indicated by a beneficial change in radiologists’ confidence level toward a correct diagnosis for both malignant and benign nodules. The average confidence ratings by 16 radiologists are shown as initial rating and 2nd rating without and with computer output, respectively, where 0 and 1.0 indicate benign and malignant, respectively.

Fig. 7

Illustration of “obvious” nodules in HRCT, in which radiologists were able to maintain their correct initial ratings for both malignant and benign nodules, even when the computer output indicated incorrect results. Thus, no serious detrimental effect due to CAD occurred in the radiologists’ ratings.

Fig. 8

Comparison of an unknown case of a mass in a mammogram in the center with two benign masses on the left and two malignant masses on the right, which may be retrieved from PACS. Most observers were able to identify the unknown case correctly as being more similar to malignant masses than to benign ones.