@article {Jug2014,
	title = {Optimal joint segmentation and tracking of escherichia coli in the mother machine},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8677},
	year = {2014},
	pages = {25{\textendash}36},
	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 {\textquotedblleft}Mother Machine{\textquotedblright} [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.},
	issn = {16113349},
	doi = {10.1007/978-3-319-12289-2_3},
	author = {Jug, Florian and Pietzsch, Tobias and Kainm{\"u}ller, Dagmar and Funke, Jan and Kaiser, Matthias and van Nimwegen, Erik and Carsten Rother and Myers, Gene}
}
