Automatic Tracking of Escherichia Coli Bacteria
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Cell population tracking and lineage construction with spatiotemporal context
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Cell tracking in microscopic video using matching and linking of bipartite graphs
Computer Methods and Programs in Biomedicine
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Automated visual-tracking of cell population in vitro using phase-contrast time-lapse microscopy is vital for the quantitative and systematic study of cell behaviors, including spatiotemporal quantification of migration, proliferation, and apoptosis. The low image quality, high and varying density of the cell culture, and the complexity of cell behaviors pose many challenges to existing tracking techniques. This paper presents a fully-automated multitarget tracking system that can simultaneously track hundreds of cells and efficiently cope with these challenges. The approach exploits a fast topology-constrained level-set method in conjunction with a stochastic motion filter, with a careful formulation that makes it suitable for real-time tracking during acquisition. Our methodology was applied to human tissue cell tracking in vitro under various imaging conditions and yielded a 88.4% tracking accuracy.