The effective classification and tracking of cells obtained from modern staining techniques has significant limitations due to the necessity of having to train and utilize a human expert in the field who must manually identify each cell in each slide. Often times these slides are filled with noise cells that are not of particular interest to the researcher. The use of computational methods has the ability to effectively and efficiently enhance image quality, as well as identify and track target cell types over large data sets. Here we present a computational approach to the in vitro tracking of T cells in time-lapse imagery capable of scaling to hundreds of cells and applicable to multiple staining techniques.
Arbuckle, C.L., Greenberg, M.L., Linstead, E.J., 2015. Detection and Tracking of T Cells in Time-lapse Imaging, in: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, BCB ’15. ACM, New York, NY, USA, pp. 545–546. doi:10.1145/2808719.2811457