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

TL;DR

This paper introduces a novel stochastic neural network-based method for automatic cell tracking and division detection in microscopy image sequences of bacterial colonies, achieving high accuracy on simulated and real data.

Contribution

It presents an innovative approach using Boltzmann machines for deformable cell tracking and division detection, advancing automated analysis in microbiology imaging.

Findings

Cell registration accuracy ranged from 94.5% to 100% on simulated data.

Real data tests showed registration accuracy from 90% to 100%.

Method effectively tracks deformable bacteria and detects divisions.

Abstract

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%.