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Our work targets automated analysis to quantify the growth dynamics of apopulation of bacilliform bacteria. We propose an innovative approach toframe-sequence tracking of deformable-cell motion by the automated minimizationof a new, specific cost functional. This minimization is implemented bydedicated Boltzmann machines (stochastic recurrent neural networks). Automateddetection of cell divisions is handled similarly by successive minimizations oftwo cost functions, alternating the identification of children pairs and parentidentification. We validate the proposed automatic cell tracking algorithmusing (i) recordings of simulated cell colonies that closely mimic the growthdynamics of E. coli in microfluidic traps and (ii) real data. On a batch of1100 simulated image frames, cell registration accuracies per frame ranged from94.5% to 100%, with a high average. Our initial tests using experimental imagesequences (i.e., real data) of E. coli colonies also yield convincing results,with a registration accuracy ranging from 90% to 100%.

Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies