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Function and Dynamic Biological Processes

Using computerized video time-lapse microscopy it is possible to monitor the dynamics of tagged proteins inside a cell, the motion of individual cells, or entire cell populations over extended periods of time. Analyzing such data sets is a challenge and constitutes a major bottleneck for the full exploitation of multidimensional microscopy sequences that document studies of biological object dynamics. Only recently has it become possible to study the dynamics of sub-cellular signaling events within the living cell. 


The ability to track cells has broad applicability and can, for example, be used to study cell migration in general, wound healing and repair, and population dynamics. In the development of new cell-tracking algorithms we build on a very significant body of computer vision research. The resulting cell-tracking information can then be utilized to detect and analyze an array of biologically relevant events.Utilizing specific dynamic fluorescent markers it is possible to provide an estimate of cell cycle phase for each individual cell (see Figure).  Ultimately we will be able to identify the behavior of individual cells, describe their differentiation patterns, and estimate their specific fate. 

Particular Areas of Interest:

  • Cell tracking
  • Estimation of cell-cycle phase
  • Characterizing different modalities of cell death
  • Analysis of cell differentiation  
  • Dynamics of cell populations


Relevant Publications:

  • D. Padfield, J. Rittscher and B. Roysam, Coupled Minimum-Cost Flow Cell Tracking for High- Throughput Quantitative Analysis, Medical Image Analysis, August 2010 [pubmed]
  • D. Padfield, J. Rittscher, N. Thomas, and B. Roysam, Spatio-Temporal Cell Cycle Phase Analysis Using Level Set Methods and Fast Marching Methods, Medical Image Analysis, vol. 13, no. 1, pp. 143155, February 2009 [pubmed]
  • D. Padfield, J. Rittscher, and B. Roysam, Spatio-Temporal Cell Segmentation and Tracking for Automated Screening, IEEE International Symposium on Biomedical Imaging, Paris, France, 2008 [IEEE Xplore]
  • D. Padfield, J. Rittscher, T. Sebastian, N. Thomas, and B. Roysam, Spatio-temporal Cell Cycle Analysis Using 3D Level Set Segmentation of Unstained Confocal Fluorescence Images, IEEE International Symposium on Biomedical Imaging, Arlington, USA, 2006 [IEEE Xplore]