Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM

Abstract

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists' goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists-as opposed to a completely automatic computer interpretation-focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous-from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects-collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more-from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis.

Figure 1

Schematic diagram of a CAD system for medical image interpretation.

Figure 2

Publications on or related to CAD in Medical Physics.

Figure 3

Components within the “black box” of a CAD system.

Figure 4

Difference-image approach to detecting nodule candidates on chest radiographs. The approach aimed to enhance the nodule with one processing filter and to suppress the anatomical background with another processing filter, with the difference resulting in an image for further analysis. Reprinted with permission from Giger et al. 1988 (Ref. 21).

Figure 5

ROC curves illustrating statistically significant improvement in radiologists’ detection of microcalcification clusters when a computer aid is used. Level 1 corresponds to use of the computer having a performance level of 87% true-positive rate and an average of four false-positive clusters per image. Level 2 corresponds to use a computer aid with the same 87% true-positive rate but a simulated average false-positive cluster rate of only one false-positive cluster per image. Reprinted with permission from Chan et al. (Ref. 114).

Figure 6

(a) First prototype CADe system—developed for screening mammography at the University of Chicago (circa 1994); (b) system annotated output on thermal paper.

Figure 7

Computer∕human interface for a multimodality workstation with computer outputs in numerical, pictorial, and graphical modes for both (a) mammography CADx output and (b) sonography CADx output.

Figure 8

Three empirical ROC curves with area under the ROC curve (AUC) values of 0.917, 0.825, and 0.862. The partial area values are 0.817, 0.484, and 0.261, respectively, for a sensitivity threshold 0.90. It is apparent that ROC (1), with its high partial area index, corresponds to a high sensitivity at high specificity (1-FPF). Reprinted with permission from Jiang et al. (Ref. 323).