The process required to track cellular structures is a key task in the study of cell migration. This allows the accurate estimation of motility indicators that help in the understanding of mechanisms behind various biological processes. This paper reports a particle-based fully automatic tracking framework that is able to quantify the motility of living cells in time-lapse images. Contrary to the standard tracking methods based on predefined motion models, in this paper we reformulate the tracking mechanism as a data driven optimization process to remove its reliance on a priory motion models. The proposed method has been evaluated using 2D and 3D deconvolved epifluorescent in-vivo image sequences that describe the development of the quail embryo.