An active particle-based tracking framework for 2D and 3D time-lapse microscopy images.
ABSTRACT 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.
Article: Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells[show abstract] [hide abstract]
ABSTRACT: Understanding cell lineage relationships is fundamental to understanding development, and can shed light on disease etiology and progression. We present a method for automated tracking of lineages of proliferative, migrating cells from a sequence of images. The method is applicable to image sequences gathered either in vitro or in vivo. Currently, generating lineage trees from progenitor cells over time is a tedious, manual process, which limits the number of cell measurements that can be practically analyzed. In contrast, the automated method is rapid and easily applied, and produces a wealth of measurements including the precise position, shape, cell-cell contacts, motility and ancestry of each cell in every frame, and accurate timings of critical events, e.g., mitosis and cell death. Furthermore, it automatically produces graphical output that is immediately accessible. Application to clonal development of mouse neural progenitor cells growing in cell culture reveals complex changes in cell cycle rates during neuron and glial production. The method enables a level of quantitative analysis of cell behavior over time that was previously infeasibleCell Cycle. 02/2006; 5(3).
Algorithms for Molecular Biology, v.4 (2009).
Article: Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis.IEEE Trans. Med. Imaging. 01/2010; 29:96-105.