Ct3d

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 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.

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References in zbMATH (referenced in 3 articles , 1 standard article )

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  1. Canzar, Stefan; Elbassioni, Khaled; Klau, Gunnar W.; Mestre, Julián: On tree-constrained matchings and generalizations (2015)
  2. Canzar, Stefan; Elbassioni, Khaled; Klau, Gunnar W.; Mestre, Julián: On tree-constrained matchings and generalizations (2011)
  3. Xiao, Hang; Li, Ying; Du, Jiulin; Mosig, Axel: \textitct3d: tracking microglia motility in 3D using a novel cosegmentation approach (2011) ioport