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The mother machine is a popular microfluidic device that allows long-termtime-lapse imaging of thousands of cells in parallel by microscopy. It hasbecome a valuable tool for single-cell level quantitative analysis andcharacterization of many cellular processes such as gene expression andregulation, mutagenesis or response to antibiotics. The automated andquantitative analysis of the massive amount of data generated by suchexperiments is now the limiting step. In particular the segmentation andtracking of bacteria cells imaged in phase-contrast microscopy---with errorrates compatible with high-throughput data---is a challenging problem.  In this work, we describe a novel formulation of the multi-object trackingproblem, in which tracking is performed by a regression of the bacteria'sdisplacement, allowing simultaneous tracking of multiple bacteria, despitetheir growth and division over time. Our method performs jointly segmentationand tracking, leveraging sequential information to increase segmentationaccuracy.  We introduce a Deep Neural Network architecture taking advantage of aself-attention mechanism which yields extremely low tracking error rate andsegmentation error rate. We demonstrate superior performance and speed comparedto state-of-the-art methods. Our method is named DiSTNet which stands forDISTance+DISplacement Segmentation and Tracking Network.  While this method is particularly well suited for mother machine microscopydata, its general joint tracking and segmentation formulation could be appliedto many other problems with different geometries.

DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine