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 less than 0.005\% tracking error rate and less than 0.03\% segmentation error rate. We demonstrate superior performance and speed compared to state-of-the-art methods  . 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.
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.
R
less than 0.005\%
extremely low
D
less than 0.03\%
A
. Our method is named DiSTNet which stands for DISTance+DISplacement Segmentation and Tracking Network