  Our work targets automated analysis to quantify the growth dynamics of a
population of bacilliform bacteria. We propose an innovative approach to
frame-sequence tracking of deformable-cell motion by the automated minimization
of a new, specific cost functional. This minimization is implemented by
dedicated Boltzmann machines (stochastic recurrent neural networks). Automated
detection of cell divisions is handled similarly by successive minimizations of
two cost functions, alternating the identification of children pairs and parent
identification. We validate the proposed automatic cell tracking algorithm
using (i) recordings of simulated cell colonies that closely mimic the growth
dynamics of E. coli in microfluidic traps and (ii) real data. On a batch of
1100 simulated image frames, cell registration accuracies per frame ranged from
94.5% to 100%, with a high average. Our initial tests using experimental image
sequences (i.e., real data) of E. coli colonies also yield convincing results,
with a registration accuracy ranging from 90% to 100%.
