title: A computational framework for particle and whole cell tracking applied to a real biological dataset.
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.
authors.affiliation: Department of Mathematics, University of Sussex, UK. Electronic address: F.W.Yang@sussex.ac.uk.
authors.forename: Feng Wei
authors.initials: FW
authors.lastname: Yang
authors.affiliation: School of Mathematics & Statistics, University of St Andrews, UK. Electronic address: cv28@st-andrews.ac.uk.
authors.forename: Chandrasekhar
authors.initials: C
authors.lastname: Venkataraman
authors.affiliation: Department of Mathematics, University of Sussex, UK. Electronic address: V.Styles@sussex.ac.uk.
authors.forename: Vanessa
authors.initials: V
authors.lastname: Styles
authors.affiliation: ibidi GmbH Am Klopferspitz 19, 82152 Martinsried, Germany. Electronic address: vkuttenberger@ibidi.de.
authors.forename: Verena
authors.initials: V
authors.lastname: Kuttenberger
authors.affiliation: ibidi GmbH Am Klopferspitz 19, 82152 Martinsried, Germany. Electronic address: ehorn@ibidi.de.
authors.forename: Elias
authors.initials: E
authors.lastname: Horn
authors.affiliation: ibidi GmbH Am Klopferspitz 19, 82152 Martinsried, Germany. Electronic address: zguttenberg@ibidi.de.
authors.forename: Zeno
authors.initials: Z
authors.lastname: von Guttenberg
authors.affiliation: Department of Mathematics, University of Sussex, UK. Electronic address: A.Madzvamuse@sussex.ac.uk.
authors.forename: Anotida
authors.initials: A
authors.lastname: Madzvamuse
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor; D002465:Cell Movement; D058948:Cell Tracking; D006801:Humans; D008853:Microscopy / Q000379:methods; D008954:Models, Biological
keywords: Cell tracking; Geometric evolution law; Optimal control; Particle tracking; Phase-contrast microscopy; Segmentation