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Rodrigo Cilla edited this page Jul 4, 2015 · 10 revisions

We release TTT in an effort to provide methods to obtain accurate descriptions of epithelial cells from in-vivo confocal microscopy timelapses. TTT processes adherens junction stained time lapses to provide a description of the cells in the epithelia and the motions and alterations transforming them. AJ Graphs, a class of cell vertex models, are recovered for each instant in the movie, providing an accurate segmentation of the cells in the tissue. We exploit the duality property of planar graphs to identify the different cells in the tissue and obtain polygon based measurements of the cells. The data extracted with TTT might be employed to characterize different morphogenetic processes such leg elongation.

TTT processes confocal image time lapses to measure and track epithelial cells from Drosophila Melanogaster

We will start releasing different algorithms we have developed in our lab as a companion to our recent PLoS Computational Biology paper

Cilla R, Mechery V, Hernandez de Madrid B, Del Signore S, Dotu I, Hatini V (2015) Segmentation and Tracking of Adherens Junctions in 3D for the Analysis of Epithelial Tissue Morphogenesis. PLoS Comput Biol 11(4): e1004124. doi:10.1371/journal.pcbi.1004124

Currently, TTT provides algorithms for Deconvolution. We are working hard to release algorithms for Hessian object detection, Anisotropic Diffusion, AJ Graphs, Cell Graphs and Cell Lineage Extraction. In the future a Qt GUI will be provided.

Although otherwise noticed, the ttt components are released under a GNU GPL v3 Licence Probably you want to take a look into the [Install].

  1. Introduction
  2. Install
  3. Deconvolution
  4. Hessian Filters
  5. AJ Graphs
  6. Cell Graphs
  7. Cell Lineage Extraction

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