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

By Maryellen Lissak Giger, Heang-Ping Chan, J. Boone. 2008

361 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy (2020, background): “Even systems with widespread adoption, such as CAD in Mammography, have been shown to require a radiologist to do more work, not less, [18] and generally do not improve a radiologist’s diagnostic accuracy [12, 26].”
Deep Learning in Medical Ultrasound Analysis: A Review (2019, background): “Traditional machine learning approaches for breast tumor or mass lesion CADx often utilize handcrafted and heuristic lesion-extracted features [85].”
A deep learning framework for supporting the classification of breast lesions in ultrasound images (2017, neutral): “of medical image interpretation and diagnosis[4, 5, 6, 7, 8, 9, 10, 11, 12, 13].”
Histopathological Image Analysis: A Review (2009, background): “While several similar reviews have been published for CAD in medical imaging and diagnostic radiology [15]–[23], to the best of our knowledge no related review has been undertaken for digitized histopathology imagery.”
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review (2015, background): “Computer vision has given rise to many promising solutions, but, instead of focusing on fully automatic computerized systems, researchers have aimed at providing computer image analysis techniques to aid radiologists in their clinical decisions [1].”
Why CAD Failed in Mammography. (2018, background): “mammograms and review of old imaging [10].”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “Many review papers have been written over the past two decades on CADx, radiomic features, and machine learning, 158,159 and thus, details will not be presented in this paper.”