@article {19371,
	title = {Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells.},
	journal = {Journal of microscopy},
	volume = {249},
	year = {2013},
	month = {2013 Jan},
	pages = {41-52},
	abstract = {We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are physically in contact with each other. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting, tracking and lineage modelling in cell biology. The segmentation method presented in this paper consists of (1) background reconstruction to obtain noise-free foreground pixels and (2) incorporation of biological insight about dividing and nondividing cells into the segmentation process to achieve reliable separation of foreground pixels defined as pixels associated with individual cells. The segmentation results for a time-lapse image stack were compared against 238 manually segmented images (8219 cells) provided by experts, which we consider as reference data. We chose two metrics to measure the accuracy of segmentation: the {\textquoteright}Adjusted Rand Index{\textquoteright} which compares similarities at a pixel level between masks resulting from manual and automated segmentation, and the {\textquoteright}Number of Cells per Field{\textquoteright} (NCF) which compares the number of cells identified in the field by manual versus automated analysis. Our results show that the automated segmentation compared to manual segmentation has an average adjusted rand index of 0.96 (1 being a perfect match), with a standard deviation of 0.03, and an average difference of the two numbers of cells per field equal to 5.39\% with a standard deviation of 4.6\%.},
	keywords = {Animals, Cell Adhesion, Cell Count, Cell Division, Cell Shape, Computational Biology, Fibroblasts, Image Processing, Computer-Assisted, Mice, Microscopy, Phase-Contrast, NIH 3T3 Cells, Reproducibility of results, Sensitivity and Specificity, Time-Lapse Imaging},
	issn = {1365-2818},
	doi = {10.1111/j.1365-2818.2012.03678.x},
	author = {Chalfoun, J and Kociolek, M and Dima, A and Halter, M and Cardone, Antonio and Peskin, A and Bajcsy, P and Brady, M.}
}
@article {19369,
	title = {Cell cycle dependent TN-C promoter activity determined by live cell imaging.},
	journal = {Cytometry. Part A : the journal of the International Society for Analytical Cytology},
	volume = {79},
	year = {2011},
	month = {2011 Mar},
	pages = {192-202},
	abstract = {The extracellular matrix protein tenascin-C plays a critical role in development, wound healing, and cancer progression, but how it is controlled and how it exerts its physiological responses remain unclear. By quantifying the behavior of live cells with phase contrast and fluorescence microscopy, the dynamic regulation of TN-C promoter activity is examined. We employ an NIH 3T3 cell line stably transfected with the TN-C promoter ligated to the gene sequence for destabilized green fluorescent protein (GFP). Fully automated image analysis routines, validated by comparison with data derived from manual segmentation and tracking of single cells, are used to quantify changes in the cellular GFP in hundreds of individual cells throughout their cell cycle during live cell imaging experiments lasting 62 h. We find that individual cells vary substantially in their expression patterns over the cell cycle, but that on average TN-C promoter activity increases during the last 40\% of the cell cycle. We also find that the increase in promoter activity is proportional to the activity earlier in the cell cycle. This work illustrates the application of live cell microscopy and automated image analysis of a promoter-driven GFP reporter cell line to identify subtle gene regulatory mechanisms that are difficult to uncover using population averaged measurements.},
	keywords = {Animals, cell cycle, Gene Expression Regulation, Green Fluorescent Proteins, Image Processing, Computer-Assisted, Mice, Microscopy, Fluorescence, Microscopy, Phase-Contrast, NIH 3T3 Cells, Promoter Regions, Genetic, Tenascin},
	issn = {1552-4930},
	doi = {10.1002/cyto.a.21028},
	author = {Halter, Michael and Sisan, Daniel R and Chalfoun, Joe and Stottrup, Benjamin L and Cardone, Antonio and Dima,Alden A. and Tona, Alessandro and Plant,Anne L. and Elliott, John T}
}
