Premium
Enabling user‐guided segmentation and tracking of surface‐labeled cells in time‐lapse image sets of living tissues
Author(s) -
Mashburn David N.,
Lynch Holley E.,
Ma Xiaoyan,
Hutson M. Shane
Publication year - 2012
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.22034
Subject(s) - computer science , segmentation , computer vision , focus (optics) , tracking (education) , artificial intelligence , image segmentation , feature (linguistics) , suite , process (computing) , market segmentation , a priori and a posteriori , geography , psychology , pedagogy , linguistics , philosophy , physics , archaeology , epistemology , marketing , optics , business , operating system
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. © 2012 International Society for Advancement of Cytometry