Automated characterization of cell shape changes during amoeboid motility by skeletonization

Autor: Peter N. Devreotes, Cathryn Kabacoff, Yuan Xiong, Douglas N. Robinson, Jonathan Franca-Koh, Pablo A. Iglesias
Rok vydání: 2009
Předmět:
Motility
Image processing
02 engineering and technology
Biology
Methodology article
Models
Biological
Skeletonization
Bone and Bones
03 medical and health sciences
Automation
Species Specificity
Structural Biology
Cell Movement
Modelling and Simulation
0202 electrical engineering
electronic engineering
information engineering
Image Processing
Computer-Assisted
Animals
Cluster Analysis
Computer vision
Dictyostelium
Pseudopodia
Amoeba
Process (anatomy)
Molecular Biology
lcsh:QH301-705.5
Cell Shape
030304 developmental biology
0303 health sciences
business.industry
Applied Mathematics
Systems Biology
Image segmentation
Computer Science Applications
Phenotype
lcsh:Biology (General)
Modeling and Simulation
020201 artificial intelligence & image processing
Artificial intelligence
business
Biological system
Pruning (morphology)
Smoothing
Algorithms
Zdroj: BMC Systems Biology
BMC Systems Biology, Vol 4, Iss 1, p 33 (2010)
ISSN: 1752-0509
Popis: Background The ability of a cell to change shape is crucial for the proper function of many cellular processes, including cell migration. One type of cell migration, referred to as amoeboid motility, involves alternating cycles of morphological expansion and retraction. Traditionally, this process has been characterized by a number of parameters providing global information about shape changes, which are insufficient to distinguish phenotypes based on local pseudopodial activities that typify amoeboid motility. Results We developed a method that automatically detects and characterizes pseudopodial behavior of cells. The method uses skeletonization, a technique from morphological image processing to reduce a shape into a series of connected lines. It involves a series of automatic algorithms including image segmentation, boundary smoothing, skeletonization and branch pruning, and takes into account the cell shape changes between successive frames to detect protrusion and retraction activities. In addition, the activities are clustered into different groups, each representing the protruding and retracting history of an individual pseudopod. Conclusions We illustrate the algorithms on movies of chemotaxing Dictyostelium cells and show that our method makes it possible to capture the spatial and temporal dynamics as well as the stochastic features of the pseudopodial behavior. Thus, the method provides a powerful tool for investigating amoeboid motility.
Databáze: OpenAIRE
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