<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jug, Florian</style></author><author><style face="normal" font="default" size="100%">Pietzsch, Tobias</style></author><author><style face="normal" font="default" size="100%">Kainmüller, Dagmar</style></author><author><style face="normal" font="default" size="100%">Funke, Jan</style></author><author><style face="normal" font="default" size="100%">Kaiser, Matthias</style></author><author><style face="normal" font="default" size="100%">van Nimwegen, Erik</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Myers, Gene</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal joint segmentation and tracking of escherichia coli in the mother machine</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">8677</style></volume><pages><style face="normal" font="default" size="100%">25–36</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 &gt; 95% (1.22 times the inter-observer error) and is on average 2 − 11 times faster than the microscope produces the raw data.</style></abstract></record></records></xml>