Whole cell tracking through the optimal control of geometric evolution laws

TL;DR

This paper introduces a novel cell tracking method based on optimal control of geometric evolution laws, integrating physical models of cell motility to improve accuracy in reconstructing cell morphologies from static images.

Contribution

It presents a new theoretical framework and algorithm for whole cell tracking driven by a physical cell motion model, formulated as an optimal control problem.

Findings

Numerical simulations demonstrate the effectiveness of the proposed approach.

The framework accurately reconstructs cell morphologies consistent with physical models.

The method integrates cell motion physics into the tracking process.

Abstract

Cell tracking algorithms which automate and systematise the analysis of time lapse image data sets of cells are an indispensable tool in the modelling and understanding of cellular phenomena. In this study we present a theoretical framework and an algorithm for whole cell tracking. Within this work we consider that "tracking" is equivalent to a dynamic reconstruction of the whole cell data (morphologies) from static image datasets. The novelty of our work is that the tracking algorithm is driven by a model for the motion of the cell. This model may be regarded as a simplification of a recently developed physically meaningful model for cell motility. The resulting problem is the optimal control of a geometric evolution law and we discuss the formulation and numerical approximation of the optimal control problem. The overall goal of this work is to design a framework for cell tracking within which the recovered data reflects the physics of the forward model. A number of numerical simulations are presented that illustrate the applicability of our approach.