Technical Report Number
We have developed and tested an automated method for simultaneous 3D tracking of numerous, flourescently-tagged cells. The procedure uses multiple thresholding to segment individual cells at a starting timepoint, and then iteratively applies a template-matching algorithm to locate a particular cell's position at subsequent time points. To speed up the method, we have developed a distributed implementation in which template matching is carried out in parallel on several different server machines. The distributed implementation showed a monotonic decrease in response time with increasing number of servers (up to 15 tested), demonstrating that the tracking algorithm is well suited to parallelization, and that nearly real-time performance could be expected on a parallel processor. Of four different template matching statistics tested for 3D tracking of amebae from the cellular slime mold Dictyostelium discoideum, we found that the automated procedure performed best when using a correlation statistic for matching. Using this statistic, the method achieved a .985% success rate in correctly identifying a cell from one timepoint to the next. This method is now being used regularly for 3D tracking of normal and mutant cells of D. discoideum, and as such provides a means to quantify the motion of many cells within a three-dimensional tissue mass.
Awasthi, Vikas; Doolittle, Keith W.; Parulkar, Guru; and McNally, James G., "Cell Tracking using a Distributed Algorithm for 3D Image Segmentation" Report Number: WUCS-94-22 (1994). All Computer Science and Engineering Research.