A computational framework for particle and whole cell tracking applied to a real biological dataset

Yang, Feng Wei, Venkataraman, Chandrasekhar, Styles, Vanessa, Kuttenberger, V, Horn, E, von Guttenberg, Z and Madzvamuse, Anotida (2016) A computational framework for particle and whole cell tracking applied to a real biological dataset. Journal of Biomechanics, 49 (8). pp. 1290-1304. ISSN 0021-9290

[img] PDF (Article) - Published Version
Available under License Creative Commons Attribution-NonCommercial No Derivatives.

Download (3MB)


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.

Item Type: Article
Keywords: Cell tracking; Segmentation; Particle tracking; Optimal control; Phase-contrast microscopy; Geometric evolution law
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
Subjects: Q Science
Q Science > QA Mathematics
Q Science > QA Mathematics > QA0297 Numerical analysis
Q Science > QA Mathematics > QA0299 Analysis. Including analytical methods connected with physical problems
Q Science > QA Mathematics > QA0076 Computer software
Depositing User: Anotida Madzvamuse
Date Deposited: 21 Mar 2016 11:44
Last Modified: 15 Mar 2017 01:12
URI: http://sro.sussex.ac.uk/id/eprint/60114

View download statistics for this item

📧 Request an update
Project NameSussex Project NumberFunderFunder Ref
Unravelling new mathematics for 3D cell migrationG1438LEVERHULME TRUSTRPG-2014-149
Mathematical Modelling and Analysis of Spatial Patterning on Evolving SurfacesG0872EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCILEP/J016780/1
InCeM: Research Training Network on Integrated Component Cycling in Epithelial Cell MotilityG1546EUROPEAN UNION642866 - InCeM
Kickstart: Software development and commercialisation of cell tracking algorithmsG1494UNIVERSITY OF SUSSEXUnset