DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine
This work addresses the bottleneck of analyzing massive data from single-cell experiments, enabling more efficient quantitative studies of cellular processes like gene expression and antibiotic response.
The authors tackled the problem of automated segmentation and tracking of bacteria cells in high-throughput microscopy data from the mother machine, achieving extremely low error rates in both tracking and segmentation. They introduced DiSTNet, a deep neural network that performs joint segmentation and tracking via displacement regression, demonstrating superior performance and speed compared to state-of-the-art methods.
The mother machine is a popular microfluidic device that allows long-term time-lapse imaging of thousands of cells in parallel by microscopy. It has become a valuable tool for single-cell level quantitative analysis and characterization of many cellular processes such as gene expression and regulation, mutagenesis or response to antibiotics. The automated and quantitative analysis of the massive amount of data generated by such experiments is now the limiting step. In particular the segmentation and tracking of bacteria cells imaged in phase-contrast microscopy---with error rates compatible with high-throughput data---is a challenging problem. In this work, we describe a novel formulation of the multi-object tracking problem, in which tracking is performed by a regression of the bacteria's displacement, allowing simultaneous tracking of multiple bacteria, despite their growth and division over time. Our method performs jointly segmentation and tracking, leveraging sequential information to increase segmentation accuracy. We introduce a Deep Neural Network architecture taking advantage of a self-attention mechanism which yields extremely low tracking error rate and segmentation error rate. We demonstrate superior performance and speed compared to state-of-the-art methods. Our method is named DiSTNet which stands for DISTance+DISplacement Segmentation and Tracking Network. While this method is particularly well suited for mother machine microscopy data, its general joint tracking and segmentation formulation could be applied to many other problems with different geometries.