BeadNet: deep learning-based bead detection and counting in low-resolution microscopy images.

Journal: Bioinformatics (Oxford, England)
Published Date: Nov 1, 2020

Abstract

MOTIVATION: An automated counting of beads is required for many high-throughput experiments such as studying mimicked bacterial invasion processes. However, state-of-the-art algorithms under- or overestimate the number of beads in low-resolution images. In addition, expert knowledge is needed to adjust parameters.

Authors

  • Tim Scherr
    Institut für Automation und angewandte Informatik, Karlsruher Institut für Technologie, Eggenstein-Leopoldshafen.
  • Karolin Streule
    Institute of Biological and Chemical Systems - Functional Molecular Systems, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen 76344, Germany.
  • Andreas Bartschat
    Institut für Automation und angewandte Informatik, Karlsruher Institut für Technologie, Eggenstein-Leopoldshafen.
  • Moritz Böhland
    Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.
  • Johannes Stegmaier
    Institute for Applied Computer Science, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.
  • Markus Reischl
    Institut für Automation und angewandte Informatik, Karlsruher Institut für Technologie, Eggenstein-Leopoldshafen.
  • Véronique Orian-Rousseau
    Institute of Biological and Chemical Systems - Functional Molecular Systems, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen 76344, Germany.
  • Ralf Mikut
    Institute for Applied Computer Science, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany.

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