Abstract
Cell tracking is becoming increasingly important in cell biology as it provides a valuable tool for analysing experimental data and hence furthering our understanding of dynamic cellular phenomena. The advent of high-throughput, high-resolution microscopy and imaging techniques means that a wealth of large data is routinely generated in many laboratories. Due to the sheer magnitude of the data involved manual tracking is often cumbersome and the development of computer algorithms for automated cell tracking is thus highly desirable.In this work, we describe two approaches for automated cell tracking. Firstly, we consider particle tracking. We propose a few segmentation techniques for the detection of cells migrating in a non-uniform background, centroids of the segmented cells are then calculated and linked from frame to frame via a nearest-neighbour approach. Secondly, we consider the problem of whole cell tracking in which one wishes to reconstruct in time whole cell morphologies. Our approach is based on fitting a mathematical model to the experimental imaging data with the goal being that the physics encoded in the model is reflected in the reconstructed data. The resulting mathematical problem involves the optimal control of a phase-field formulation of a geometric evolution law. Efficient approximation of this challenging optimal control problem is achieved via advanced numerical methods for the solution of semilinear parabolic partial differential equations (PDEs) coupled with parallelisation and adaptive resolution techniques.Along with a detailed description of our algorithms, a number of simulation results are reported on. We focus on illustrating the effectivity of our approaches by applying the algorithms to the tracking of migrating cells in a dataset which reflects many of the challenges typically encountered in microscopy data.
Highlights
Cell migration is an essential part of many normal biological processes and diseases (Friedl and Gilmour, 2009)
Bio-laboratories nowadays produce a huge amount of data in multi-dimensions e.g., microscopy images, that is far beyond the capacity of manual analysis in order to make informed decisions about cell shape evolution and migration trajectories (Maska et al, 2014)
Since the most competitive method from the challenge series, KTH-SE (Maska et al, 2014), used global thresholding followed by a watershed transform for splitting clusters as their segmentation technique, based on the results presented in Figs. 2 and 3 we believe that this technique may not work well on our problem, because of the heterogeneous background and the halo artefacts
Summary
Cell migration is an essential part of many normal biological processes and diseases (Friedl and Gilmour, 2009). The focus of this work is to present techniques to solve the challenging problems that arise when one seeks to automate reconstruction of cell shape evolution and cell migration trajectories from static data. For particle tracking, described, we treat each cell as a single object (i.e., a dot) and seek to determine the speed and direction of cell centroid trajectories The latter approach, illustrated, focusses on recovering dynamic cell morphologies and typically is of use to study a single cell or multiple cells in a low density setting. This resulting mathematical problem is formulated as the optimal control of a geometric evolution law (DuChateau and Zachmann, 1989; Rektorys, 1999)
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