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

arXiv: https://arxiv.org/abs/2104.13482 · HTML: https://mlsanity.com/paper/2104.13482/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 128 references that are in the map.
- First posted on arXiv 27 Apr 2021.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [Fast Optical Flow using Dense Inverse Search](https://mlsanity.com/paper/1603.03590/) (2016)
- [Deep Learning for Visual Tracking: A Comprehensive Survey](https://mlsanity.com/paper/1912.00535/) (2019)
- [Disciplined Convex-Concave Programming](https://mlsanity.com/paper/1604.02639/) (2016)
- [Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks](https://mlsanity.com/paper/1806.02070/) (2018)
- [MPM: Joint Representation of Motion and Position Map for Cell Tracking](https://mlsanity.com/paper/2002.10749/) (2020)
- [A Lagrangian Gauss-Newton-Krylov Solver for Mass- and Intensity-Preserving Diffeomorphic Image Registration](https://mlsanity.com/paper/1703.04446/) (2017)
- [CLAIRE: A distributed-memory solver for constrained large deformation diffeomorphic image registration](https://mlsanity.com/paper/1808.04487/) (2018)
- [Weakly-Supervised Cell Tracking via Backward-and-Forward Propagation](https://mlsanity.com/paper/2007.15258/) (2020)
- [An inexact Newton-Krylov algorithm for constrained diffeomorphic image registration](https://mlsanity.com/paper/1408.6299/) (2014)
- [Joint Multi-frame Detection and Segmentation for Multi-cell Tracking](https://mlsanity.com/paper/1906.10886/) (2019)
- [Tracking Single-Cells in Overcrowded Bacterial Colonies](https://mlsanity.com/paper/1706.07362/) (2017)
- [Optical Flow on Evolving Surfaces with Space and Time Regularisation](https://mlsanity.com/paper/1310.0322/) (2013)
- [Active skeleton for bacteria modeling](https://mlsanity.com/paper/1507.06504/) (2015)

## Built on by (most cited first, 1 of 1)

- [Cell tracking for live-cell microscopy using an activity-prioritized assignment strategy](https://mlsanity.com/paper/2210.11441/) (2022)

## Abstract (arXiv)

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%.