International Conference on Medical Image Computing and Computer-Assisted Intervention

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp 276-283

You Should Use Regression to Detect Cells

  • Philipp Kainz
  • Martin Urschler
  • Samuel Schulter
  • Paul Wohlhart
  • Vincent Lepetit
Conference paper

DOI: 10.1007/978-3-319-24574-4_33

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)
Cite this paper as:
Kainz P., Urschler M., Schulter S., Wohlhart P., Lepetit V. (2015) You Should Use Regression to Detect Cells. In: Navab N., Hornegger J., Wells W., Frangi A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Springer, Cham

Abstract

Automated cell detection in histopathology images is a hard problem due to the large variance of cell shape and appearance. We show that cells can be detected reliably in images by predicting, for each pixel location, a monotonous function of the distance to the center of the closest cell. Cell centers can then be identified by extracting local extremums of the predicted values. This approach results in a very simple method, which is easy to implement. We show on two challenging microscopy image datasets that our approach outperforms state-of-the-art methods in terms of accuracy, reliability, and speed. We also introduce a new dataset that we will make publicly available.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Philipp Kainz
    • 1
  • Martin Urschler
    • 2
    • 3
  • Samuel Schulter
    • 2
  • Paul Wohlhart
    • 2
  • Vincent Lepetit
    • 2
  1. 1.Institute of BiophysicsMedical University of GrazGrazAustria
  2. 2.Institute for Computer Graphics and Vision, BioTechMedGraz University of TechnologyGrazAustria
  3. 3.Ludwig Boltzmann Institute for Clinical Forensic ImagingGrazAustria

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