Histopathological Image Analysis: A Review <s> B. Differences in CAD Approaches Between Radiology and Histopathology <s> Computer-aided diagnosis (CAD) systems in mammography have been developed and investigated for several years and recently they have left the research stage to enter the clinical stage. The purpose of this article is to review the present situation of mammography for breast cancer detection and the role played by CAD systems. Results from the recent literature show that CAD systems have the potential to improve the sensitivities of radiologists in the detection of malignant clustered microcalcifications and masses, while keeping specificities at acceptable levels. This leads to the conclusion that CAD systems can be incorporated into clinical practice as a double reading option to radiologists. However, some issues have yet to be tackled for CAD systems to gain better acceptance and more widespread use worldwide. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> B. Differences in CAD Approaches Between Radiology and Histopathology <s> 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. <s> BIB002
While CAD is now being used in radiology in conjunction with a wide range of body regions and a variety of imaging modalities, the preponderant question has been: can CAD enable disease detection? Note that this question, as opposed to more diagnostic questions, is motivated by the inherent limitation in spatial resolution of radiological data. For instance, in mammography, CAD methods have been developed to automatically identify or classify mammographic lesions. In histopathology, on the other hand, simply identifying presence or absence of cancer or even the precise spatial extent of cancer may not hold as much interest as more sophisticated questions such as: what is the grade of cancer? Further, at the histological (microscopic) scale one can begin to distinguish between different histological subtypes of cancer, which is quite impossible (or at the very least difficult) at the coarser radiological scale. It is fair to say that since CAD in histopathology is still evolving, the questions that researchers have started to ask of pathology data are not as well articulated as some of the problems being investigated in radiology. A possible reason for this is that image analysis scientists are still trying to come to terms with the enormous density of data that histopathology holds compared to radiology. For instance, the largest radiological datasets obtained on a routine basis are high-resolution chest CT scans comprising approximately 512 512 512 spatial elements or million voxels. A single core of prostate biopsy tissue digitized at 40 resolution is approximately 15 000 15 000 elements or million pixels. To put this in context, a single prostate biopsy procedure can comprise anywhere between 12 and 20 biopsy samples or approximately 2.5-4 billion pixels of data generated per patient study. Due to their relatively large size and the content, these images frequently need to be processed in a multiresolution framework. Also, while radiological CAD systems mostly deal with grayscale images, histological CAD systems often need to process color images. Furthermore, with the recent advent of multispectral and hyperspectral imaging, each pixel in a histopathology section could potentially be associated with several hundred sub-bands and wavelengths. These fundamental differences in radiology and histopathology data have resulted in specialized CAD schemes for histopathology. While several similar reviews have been published for CAD in medical imaging and diagnostic radiology BIB002 - BIB001 , to the best of our knowledge no related review has been undertaken for digitized histopathology imagery. A survey for CAD histopathology is particularly relevant given that the approaches and questions being asked of histological data are different from radiological data. The motivation of this paper is to present a comprehensive review of the state-of-the-art CAD methods and the techniques employed for automated image analysis of digitized histopathology imagery.