Seeded Watersheds for Combined Segmentation and Tracking of Cells

  • Amalka Pinidiyaarachchi
  • Carolina Wählby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds from the current frame, and over-segmentation is reduced by rule-based merging, propagating labels from one time-frame to the next. Thus, watershed segmentation is used for segmentation as well as tracking of cells over time. The described algorithm was tested on neural stem/progenitor cells imaged using time-lapse microscopy. Tracking results agreed to 71% to manual tracking results. The results were also compared to tracking based on solving the assignment problem using a modified version of the auction algorithm.


Catchment Basin Watershed Segmentation Merging Step Auction Algorithm Background Seed 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Amalka Pinidiyaarachchi
    • 1
    • 2
  • Carolina Wählby
    • 1
  1. 1.Centre for Image AnalysisUppsala UniversitySweden
  2. 2.Dept. of Statistics and Computer ScienceUniversity of PeradeniyaSri Lanka

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