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Ct3d: tracking microglia motility in 3D using a novel cosegmentation approach

Motivation: Cell tracking is an important method to quantitatively analyze time-lapse microscopy data. While numerous methods and tools exist for tracking cells in 2D time-lapse images, only few and very application-specific tracking tools are available for 3D time-lapse images, which is of high rel...

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Detalles Bibliográficos
Autores principales: Xiao, Hang, Li, Ying, Du, Jiulin, Mosig, Axel
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3035800/
https://www.ncbi.nlm.nih.gov/pubmed/21186244
http://dx.doi.org/10.1093/bioinformatics/btq691
Descripción
Sumario:Motivation: Cell tracking is an important method to quantitatively analyze time-lapse microscopy data. While numerous methods and tools exist for tracking cells in 2D time-lapse images, only few and very application-specific tracking tools are available for 3D time-lapse images, which is of high relevance in immunoimaging, in particular for studying the motility of microglia in vivo. Results: We introduce a novel algorithm for tracking cells in 3D time-lapse microscopy data, based on computing cosegmentations between component trees representing individual time frames using the so-called tree-assignments. For the first time, our method allows to track microglia in three dimensional confocal time-lapse microscopy images. We also evaluate our method on synthetically generated data, demonstrating that our algorithm is robust even in the presence of different types of inhomogeneous background noise. Availability: Our algorithm is implemented in the ct3d package, which is available under http://www.picb.ac.cn/patterns/Software/ct3d; supplementary videos are available from http://www.picb.ac.cn/patterns/Supplements/ct3d. Contact: axel@picb.ac.cn; forestdu@ion.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.