Optimal Joint Segmentation and Tracking of Escherichia Coli in the Mother Machine
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” . 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.
KeywordsRandom Forest Tracking Error Assignment Model Growth Line Factor Node
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- 2.Jug, F., Pietzsch, T., Preibisch, S., Tomancak, P.: Bioimage informatics in the context of Drosophila research. Methods (2014)Google Scholar
- 6.Funke, J., Anders, B., Hamprecht, F., Cardona, A., Cook, M.: Efficient automatic 3D-reconstruction of branching neurons from EM data. In: CVPR. IEEE (2012)Google Scholar
- 7.Schiegg, M., Hanslovsky, P., Kausler, B., Hufnagel, L.: Conservation Tracking. In: ICCV (2013)Google Scholar
- 8.Kolmogorov, V., Boykov, Y., Rother, C.: Applications of parametric maxflow in computer vision. In: ICCV, pp. 1–8. IEEE (2007)Google Scholar
- 10.Jones, R.: Component trees for image filtering and segmentation. In: IEEE Workshop on Nonlinear Signal and Image Analysis (1997)Google Scholar
- 12.Blake, A., Kohli, P., Rother, C.: Markov Random Fields for Vision and Image Processing. MIT Press (2011)Google Scholar
- 13.Arganda-Carreras, I., Cardona, A., Kaynig, V., Schindelin, J.: Trainable weka segmentation (May 2011), http://fiji.sc/Trainable_Weka_Segmentation
- 14.Frey, B., Kschischang, F., Loeliger, H., Wiberg, N.: Factor graphs and algorithms. In: Proceedings of the Annual Allerton Conference on Communication Control and Computing, vol. 35, pp. 666–680 (1997)Google Scholar
- 15.Schrijver, A.: Theory of Linear and Integer Programming. J. Wiley & Sons (1998)Google Scholar