title: Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues.
abstract: 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.
authors.affiliation: Department of Physics and Astronomy, Vanderbilt University, Nashville, Tennessee 37235, USA.
authors.forename: David N
authors.initials: DN
authors.lastname: Mashburn
authors.forename: Holley E
authors.initials: HE
authors.lastname: Lynch
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Ma
authors.forename: M Shane
authors.initials: MS
authors.lastname: Hutson
mesh_terms: D000818:Animals; D058948:Cell Tracking / Q000379:methods*; D004330:Drosophila; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018613:Microscopy, Confocal; D010363:Pattern Recognition, Automated; D059008:Time-Lapse Imaging / Q000379:methods*