A Bayesian Topological Framework for the Identification and Reconstruction of Subcellular Motion

Microscopy imaging allows detailed observations of intracellular movements and the acquisition of large datasets that can be fully analyzed only by automated algorithms. Here, we develop a computational method for the automatic identification and reconstruction of trajectories followed by subcellular particles captured in microscopy image data. The method operates on stacks of raw image data and computes the complete set of contained trajectories. The method utilizes topological data analysis and standard image processing techniques and makes no assumptions about the underlying dynamics besides continuity. We test the developed method successfully against artificial and experimental datasets. Application of the method on the experimental data reveals good agreement with manual tracking and benchmarking yields performance scores competitive to the existing state-of-the-art tracking methods.