Optimal Joint Segmentation and Tracking of Escherichia Coli in the Mother Machine

  • Florian Jug
  • Tobias Pietzsch
  • Dagmar Kainmüller
  • Jan Funke
  • Matthias Kaiser
  • Erik van Nimwegen
  • Carsten Rother
  • Gene Myers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8677)

Abstract

We introduce a graphical model for the joint segmentation and tracking of E. coli cells from time lapse videos. In our setup cells are grown in narrow columns (growth channels) in a so-called “Mother Machine” [1]. In these growth channels, cells are vertically aligned, grow and divide over time, and eventually leave the channel at the top. The model is built on a large set of cell segmentation hypotheses for each video frame that we extract from data using a novel parametric max-flow variation. Possible tracking assignments between segments across time, including cell identity mapping, cell division, and cell exit events are enumerated. Each such assignment is represented as a binary decision variable with unary costs based on image and object features of the involved segments. We find a cost-minimal and consistent solution by solving an integer linear program. We introduce a new and important type of constraint that ensures that cells exit the Mother Machine in the correct order. Our method finds a globally optimal tracking solution with an accuracy of > 95% (1.22 times the inter-observer error) and is on average 2 − 11 times faster than the microscope produces the raw data.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Florian Jug
    • 1
  • Tobias Pietzsch
    • 1
  • Dagmar Kainmüller
    • 1
  • Jan Funke
    • 2
  • Matthias Kaiser
    • 3
  • Erik van Nimwegen
    • 3
  • Carsten Rother
    • 4
  • Gene Myers
    • 1
  1. 1.Max Planck Institute of Molecular Cell Biology and GeneticsGermany
  2. 2.Institute of Neuroinformatics, Univerity Zurich / ETH ZurichSwitzerland
  3. 3.Biozentrum, University of Basel, and Swiss Institute of BioinformaticsSwitzerland
  4. 4.Computer Vision Lab DresdenTechnical University DresdenGermany

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