Journal of Cellular Biochemistry

A Novel Segmentation-Based Algorithm for the Quantification of Magnified Cells

Authors

  • Gemma C. Thompson,

    1. Laboratory of Molecular Neuroscience, The Brain and Mind Research Institute, The University of Sydney, Camperdown, NSW, Australia
    Search for more papers by this author
  • Timothy A. Ireland,

    1. Laboratory of Molecular Neuroscience, The Brain and Mind Research Institute, The University of Sydney, Camperdown, NSW, Australia
    2. Biomedical Technology Services, Queensland Health, Gold Coast University Hospital, Southport, QLD, Australia
    Search for more papers by this author
  • Xanthe C. Larkin,

    1. Discipline of Pharmacology, School of Medical Science, The University of Sydney, Sydney, NSW, Australia
    2. The Brain and Mind Research Institute, The University of Sydney, Camperdown, NSW, Australia
    Search for more papers by this author
  • Jonathon Arnold,

    1. Discipline of Pharmacology, School of Medical Science, The University of Sydney, Sydney, NSW, Australia
    2. The Brain and Mind Research Institute, The University of Sydney, Camperdown, NSW, Australia
    Search for more papers by this author
  • R. M. Damian Holsinger

    Corresponding author
    1. Laboratory of Molecular Neuroscience, The Brain and Mind Research Institute, The University of Sydney, Camperdown, NSW, Australia
    2. Discipline of Biomedical Science, School of Medical Sciences, Sydney Medical School, The University of Sydney, Lidcombe, NSW, Australia
    • Correspondence to: R. M. Damian Holsinger, Laboratory of Molecular Neuroscience, The Brain and Mind Research Institute, The University of Sydney, Camperdown, NSW 2050, Australia.

      E-mail: damian.holsinger@sydney.edu.au

    Search for more papers by this author

Errata

This article is corrected by:

  1. Errata: Thompson, Gemma C., Ireland, Timothy A., Larkin, Xanthe E., Arnold, Jonathon C. and Holsinger, R. M. Damian. 2014, A novel segmentation-based algorithm for the quantification of magnified cells. J Cell Biochem, 115:1849–1854. doi: 10.1002/jcb.24882 Volume 116, Issue 1, 203, Article first published online: 11 November 2014

  • Gemma C. Thompson and Timothy Ireland contributed equally to this work.
  • The authors declare that they have no conflict of interest.

ABSTRACT

Cell segmentation and counting is often required in disciplines such as biological research and medical diagnosis. Manual counting, although still employed, suffers from being time consuming and sometimes unreliable. As a result, several automated cell segmentation and counting methods have been developed. A main component of automated cell counting algorithms is the image segmentation technique employed. Several such techniques were investigated and implemented in the present study. The segmentation and counting was performed on antibody stained brain tissue sections that were magnified by a factor of 40. Commonly used methods such as the circular Hough transform and watershed segmentation were analysed. These tests were found to over-segment and therefore over-count samples. Consequently, a novel cell segmentation and counting algorithm was developed and employed. The algorithm was found to be in almost perfect agreement with the average of four manual counters, with an intraclass correlation coefficient (ICC) of 0.8. J. Cell. Biochem. 115: 1849–1854, 2014. © 2014 Wiley Periodicals, Inc.

Ancillary