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The Future: Computer-Aided Detection

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Part of the Medical Radiology book series (Med Radiol Diagn Imaging)

During the past decade, computer-aided diagnosis (CAD) has been shown to be of clinical benefit in fields such as detection of microcalcifications and classification of masses in mammograms (Astley and Gilbert 2004). The concept of CAD is not unique to these fields; indeed, it is more important and beneficial for examinations in which a large quantity of images need to be interpreted rapidly for finding a lesion with low incidence, such as the detection of polyps in CT colonography (CTC) and the detection of lung nodules in thoracic CT scans. In its most general form, CAD can be defined as a diagnosis made by a radiologist who uses the output of a computerized scheme for automated image analysis as a diagnostic aid (Doi 2004). Conventionally, CAD acts as a “second reader,” pointing out abnormalities to the radiologist that otherwise might have been missed. The final diagnosis is made by the radiologist. This definition emphasizes the intent of CAD to support rather than substitute the human reader in the detection of polyps.

CAD for CTC typically refers to a computerized scheme for automated detection of polyps and masses in CTC data. It provides the locations of suspicious polyps and masses to radiologists. This offers a second opinion that has the potential to improve radiologists' detection performance and to reduce variability of the diagnostic accuracy among radiologists, without sig-nificantly increasing the reading time. Such a CAD scheme should be distinguished from semi-automated computer applications in radiology that automate only one of these components and depend on user interaction for the remaining tasks. A typical example is the 3D visualization of semi-automatically segmented organs (e.g., segmentation of the liver, endoluminal visualization of the colon and bronchus), or image processing of a part of an organ for generation of an image that is more easily interpreted by human readers (e.g., peripheral equalization of the breast in mammo-grams, digital subtraction bowel cleansing in virtual colonoscopy).

Keywords

  • Detection Performance
  • Colonic Wall
  • Optical Colonoscopy
  • Colonic Lumen
  • Human Reader

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Yoshida, H. (2010). The Future: Computer-Aided Detection. In: Lefere, P., Gryspeerdt, S. (eds) Virtual Colonoscopy. Medical Radiology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79886-6_14

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