Optimal joint segmentation and tracking of escherichia coli in the mother machine

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Item Type:	Article
Title:	Optimal joint segmentation and tracking of escherichia coli in the mother machine
Creators Name:	Jug, F., Pietzsch, T., Kainmüller, D., Funke, J., Kaiser, M., van Nimwegen, E., Rother, C. and Myers, G.
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.
Keywords:	Random Forest, Tracking Error, Assignment Model, Growth Line, Factor Node
Source:	Lecture Notes in Computer Science
Title of Book:	Bayesian and grAphical Models for Biomedical Imaging
ISSN:	0302-9743
ISBN:	978-3-319-12288-5
Publisher:	Springer
Volume:	8677
Page Range:	25-36
Date:	2014
Official Publication:	https://doi.org/10.1007/978-3-319-12289-2_3

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