Anniversary Paper: History and status of CAD and quantitative image analysis: The role ofMedical Physicsand 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.

Responsible Radiomics Research for Faster Clinical Translation

“…Imaging modalities such as 18 F-FDG PET, CT, and MRI are minimally invasive and would constitute an immense source of potential data for decoding tumor phenotypes (2). Computer-aided diagnosis methods and systems exploiting medical images have been developed for decades, but their wide clinical implementation has been hampered by false-positive rates (3). As a consequence, routine clinical exploitation of images still consists mostly of visual or manual assessments.…”

Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

“…Therefore, several enhancement filters for specific lesions have been developed in the CAD field [36][37][38][39][40][41][42]. Giger et al developed a difference image technique for the detection of lung nodules in radiographs, based on the subtraction of the objectsuppressed image using a ring filter from the object-enhanced image using an object-matched filter [36,43]. Arimura et al applied the difference image technique to the automated detection of lung nodules in low-dose CT [44].…”

“…Probabilistic atlases could store information on anatomic and functional variability in a population. We have applied a multiple-gray level thresholding technique developed by Giger et al [21,43] to the neuroradiological CAD field, for detection of initial aneurysm candidates, white matter hyperintensities (WMHs) and MS candidates in MR images as the points with local maximum pixel values in candidate regions [25,26,28,30,31]. Figure 6 shows a FLAIR image including initial MS candidate points (white dots) obtained by the multiple gray level thresholding technique, and a brain image with initial candidate regions (white solid lines) segmented by the region growing technique [30,31].…”

An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs

“…[4][5][6][7] Three main research projects of CAD scheme have been investigated: 7 quantitative analysis of lesions involved in vascular imaging; detection of lung nodules and interstitial lung diseases in chest radiographs; and detection of clustered microcalcifications in mammograms.…”