Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues.

Mashburn DN, Lynch HE, Ma X, Hutson MS
Cytometry A. 2012 81 (5): 409-18

PMID: 22411907 · PMCID: PMC3331924 · DOI:10.1002/cyto.a.22034

To study the process of morphogenesis, one often needs to collect and segment time-lapse images of living tissues to accurately track changing cellular morphology. This task typically involves segmenting and tracking tens to hundreds of individual cells over hundreds of image frames, a scale that would certainly benefit from automated routines; however, any automated routine would need to reliably handle a large number of sporadic, and yet typical problems (e.g., illumination inconsistency, photobleaching, rapid cell motions, and drift of focus or of cells moving through the imaging plane). Here, we present a segmentation and cell tracking approach based on the premise that users know their data best-interpreting and using image features that are not accounted for in any a priori algorithm design. We have developed a program, SeedWater Segmenter, that combines a parameter-less and fast automated watershed algorithm with a suite of manual intervention tools that enables users with little to no specialized knowledge of image processing to efficiently segment images with near-perfect accuracy based on simple user interactions.

Copyright © 2012 International Society for Advancement of Cytometry.

MeSH Terms (9)

Animals Cell Tracking Drosophila Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Imaging, Three-Dimensional Microscopy, Confocal Pattern Recognition, Automated Time-Lapse Imaging

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