• DocumentCode
    77909
  • Title

    Automated Histology Analysis: Opportunities for signal processing

  • Author

    McCann, M.T. ; Ozolek, J.A. ; Castro, C.A. ; Parvin, B. ; Kovacevic, J.

  • Author_Institution
    Biomed. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
  • Volume
    32
  • Issue
    1
  • fYear
    2015
  • fDate
    Jan. 2015
  • Firstpage
    78
  • Lastpage
    87
  • Abstract
    Histology is the microscopic inspection of plant or animal tissue. It is a critical component in diagnostic medicine and a tool for studying the pathogenesis and biology of processes such as cancer and embryogenesis. Tissue processing for histology has become increasingly automated, drastically increasing the speed at which histology labs can produce tissue slides for viewing. Another trend is the digitization of these slides, allowing them to be viewed on a computer rather than through a microscope. Despite these changes, much of the routine analysis of tissue sections remains a painstaking, manual task that can only be completed by highly trained pathologists at a high cost per hour. There is, therefore, a niche for image analysis methods that can automate some aspects of this analysis. These methods could also automate tasks that are prohibitively time-consuming for humans, e.g., discovering new disease markers from hundreds of whole-slide images (WSIs) or precisely quantifying tissues within a tumor.
  • Keywords
    cancer; medical image processing; tissue engineering; tumours; animal tissue; automated histology analysis; cancer; diagnostic medicine; disease markers; embryogenesis; image analysis method; microscopic inspection; pathogenesis; plant tissue; signal processing; tissue processing; tissue sections; tumor; Biological tissues; Biomedical imaging; Biomedical signal processing; Biopsy; Histology; Image analysis; Image color analysis; Microscopy; Signal processing; Visualization;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1053-5888
  • Type

    jour

  • DOI
    10.1109/MSP.2014.2346443
  • Filename
    6975290