F Jug, T Pietzsch, D Kainmüller, J Funke, M Kaiser, E van Nimwegen, C Rother, G Myers
Optimal joint segmentation and tracking of Escherichia coli in the mother machine, Bayesian and grAphical Models for Biomedical Imaging. Lecture Notes in Computer Science Volume 8677, 2014, pp 25-36,
doi: 10.1007/978-3-319-12289-2_3

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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