Evolutionary optimization of image processing for cell detection in microscopy images

Andreas Haghofer, Sebastian Dorl, Andre Oszwald, Johannes Breuss, Jaroslaw Jacak, Stephan M. Winkler

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this paper, we present a new evolution-based algorithm that optimizes cell detection image processing workflows in a self-adaptive fashion. We use evolution strategies to optimize the parameters for all steps of the image processing pipeline and improve cell detection results. The algorithm reliably produces good cell detection results without the need for extensive domain knowledge. Our algorithm also needs no labeled data to produce good cell detection results compared to the state-of-the-art neural network approaches. Furthermore, the algorithm can easily be adapted to different applications by modifying the processing steps in the pipeline and has high scalability since it supports multithreading and computation on graphical processing units (GPUs).

Original languageEnglish
Pages (from-to)17847-17862
Number of pages16
JournalSoft Computing
Issue number23
Publication statusPublished - Dec 2020


  • Computer vision
  • Evolutionary algorithms
  • Heuristic optimization
  • Image processing
  • Machine learning


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