You Should Use Regression to Detect Cells

  • Philipp Kainz
  • Martin Urschler
  • Samuel Schulter
  • Paul Wohlhart
  • Vincent Lepetit
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)


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.


Ground Truth Cell Center Image Patch Cell Detection Histopathological Image 
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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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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