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NeuroMorphoVis provides four major toolboxes that can be used for
- Automated analysis of neuronal morphology skeletons that are digitally reconstructed from optical microscopy stacks.
- An easy context to load broken morphology skeletons and repair them manually.
- Sketching and building three-dimensional representations of the morphology skeletons using various methods for visual analytics.
- Automated reconstruction of accurate three-dimensional somata profiles, even with classical morphology skeletons that do not have any three-dimensional data of their somata. This approach uses the physics engine of Blender based on Hooke's law and mass spring models.
- Automated reconstruction of polygonal mesh models that represent the membranes of the neuronal morphologies based on the piecewise meshing method presented by Abdellah et al., 2017.
- Fast mesh reconstruction based on skinning and union operators for artistic rendering.
- Accurate mesh reconstruction with meta balls to create watertight meshes for reaction-diffusion simulations.
- Automated generation of high quality media for scientific documents and publications using different shading styles and materials.
- Multiple interfaces: user-friendly graphical user interface, a rich command line interface, editable configuration files and a high level python API for python scripting.
- Importing morphologies in multiple file formats including SWC, H5 or even from a BBP circuit using GIDs and cell targets.
- Exporting the reconstructed meshes in several file formats including PLY, OBJ, STL and also as a Blender file (.blend).
- Parallel batch processing on multi-node visualization clusters using SLURM workload manager.
The end users are recommended to download the archives from the links provided in the previous section. But if the users are willing to contribute and extend NeuroMorphoVis, we recommend to install it as described in this installation guide.
NeuroMorphoVis is primarily designed as a plug-in in Blender. It comes with a user-friendly GUI and a rich set of command line options. Moreover, the tool is configurable via input configuration files making it possible to link it to web interface or using it on massively parallel visualization clusters for batch production.
The tool is also extensible via its powerful python API that can be imported in Blender console and text browser.
To make it accessible to end users with minimal programming knowledge or even with no programming experience at all, the core functionality of NeuroMorphoVis is exposed to users via a friendly graphical user interface that would allow them to navigate and adjust the parameters of the different toolboxes seamlessly. A detailed guide to use NeuroMorphoVis from its GUI is available in this user guide.
A screen shot of a neuronal morphology skeleton reconstructed by NeuroMorphoVis and sketched in the 3D view port of Blender. Note that the neurites are tagged with different colors (green for apical dendrite, red for basal dendrites and blue for the axon) and the soma (in orange) is reconstructed as a realistic three-dimensional profile not as a sphere.
NeuroMorphoVis has a rich command line interface that would make it easy to connect it to a web interface or use it to accomplish multiple tasks in an automated way. A list of all the command line options and their description are available in this user guide.
Users can easily configure and use NeuroMorphoVis via editable configuration files. Instructions to write and use configurations files are available in this user guide.