Tissue Image ToolKit¶
TimageTK (Tissue Image Toolkit) is a Python package dedicated to image processing of multicellular architectures, such as plants or animals. It is intended for biologists, modellers and computer scientists.
Overview¶
The package provides the following main functionalities:
Image Filtering
Linear filter operators such as: Gaussian, gradient, hessian, laplacian, …
Mathematical Morphology
Morphology operator such as: erosion, dilation, opening, closing, hat transform, sequential filters, …
Image Registration
Linear and non-linear block-matching registration, transformation operators, sequence registration, multi-angle fusion, …
Image Segmentation
h-transform, connected component labeling, watershed, …
Visualisation
Stack browser, orthogonal views, projection maps and other GUI.
Batch Processing
Simple JSON-based data structure to batch process large data sets.
Organisation¶
Jonathan Legrand
Lead developer A B CNRS
Christophe Godin
Coordinator A Inria
Grégoire Malandain
Coordinator C Inria
Teva Vernoux
Coordinator B CNRS
Guillaume Baty
Contributor
Guillaume Cerutti
Contributor A INRAe
Sophie Ribes
Contributor
Active Research Teams¶
A Inria team Mosaic, RDP-ENS Lyon, UMR5667.
B Hormonal Signalling and Development team, RDP-ENS Lyon, UMR5667.
C Inria team Morpheme, Sophia Antipolis.
Source Code Repository¶
Inria GitLab Repository¶
The Inria GitLab repository of TimageTK is maintained by members of the Mosaic team. Use the Issues section to contact us if you have difficulties or issues.
GitHub Repository¶
The GitHub repository of TimageTK is not maintained anymore, but is kept for historic reasons.
Contributing¶
We welcome all contributions to the development of TimageTK as it is intended as an open-source scientific software.
Have a look at the contribution guidelines in the documentation for more details.
Additional Notes¶
Images and other data structures can be rendered using the visualization functions we provide. However, if you are less experienced with code, using a dedicated interactive image visualization software such as Gnomon or Fiji is recommended.
TimageTK leverages the work of some of the most standard Python libraries:
For 3D visualisations, we rely on:
Other less “standard” libraries, developed by research teams, are used:
tifffile by Christoph Gohlke et al.
czifile by Christoph Gohlke et al.
vt by Grégoire Malandain et al.
vt-python by Grégoire Malandain et al.
MorphoNet by Emmanuel Faure et al.
pytorch-3dunet by Adrian Wolny & PlantSeg by Lorenzo Cerrone et al.