Open AccessCell tracking using a distributed algorithm for 3‐D image segmentation
Author(s) -
Awasthi Vikas,
Doolittle Keith W,
Parulkar Guru,
McNally James G
Publication year - 1994
Publication title -
bioimaging
Language(s) - English
Resource type - Journals
eISSN - 1361-6374
pISSN - 0966-9051
DOI - 10.1002/1361-6374(199406)2:2<98::aid-bio4>3.0.co;2-5
Subject(s) - artificial intelligence , computer vision , tracking (education) , segmentation , image segmentation , computer science , image (mathematics) , pattern recognition (psychology) , algorithm , psychology , pedagogy
We have developed and tested an automated method for simultaneous 3‐D tracking of numerous, fluorescently‐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 timepoints. 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 3‐D tracking of amoebae from the cellular slime mould Dictyostelium discoideum , we found that the automated procedure performed best when using a correlation statistic for matching. Using this statistic, the method achieved a 98.5% success rate in correctly identifying a cell from one time point to the next. This method is now being used regularly for 3‐D 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.