NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks

Abstract

MotivationFrom image stacks to computational models, processing digital representations of neuronal morphologies is essential to neuroscientific research. Workflows involve various techniques and tools, leading in certain cases to convoluted and fragmented pipelines. The existence of an integrated, extensible and free framework for processing, analysis and visualization of those morphologies is a challenge that is still largely unfulfilled.ResultsWe present NeuroMorphoVis, an interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. Our framework is capable of detecting and repairing tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico imaging studies. The applicability of NeuroMorphoVis is demonstrated with two case studies. The first simulates the construction of three-dimensional profiles of neuronal somata and the other highlights how the framework is leveraged to create volumetric models of neuronal circuits for simulating different types of in vitro imaging experiments.Availability and implementationThe source code and documentation are freely available on https://github.com/BlueBrain/NeuroMorphoVis under the GNU public license. The morphological analysis, visualization and surface meshing are implemented as an extensible Python API (Application Programming Interface) based on Blender, and the volume reconstruction and analysis code is written in C++ and parallelized using OpenMP. The framework features are accessible from a user-friendly GUI (Graphical User Interface) and a rich CLI (Command Line Interface).Supplementary information Supplementary data are available at Bioinformatics online.

Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis

“…They range from several hundred for simple morphologies and up to a few thousand for a neuron with highly complex arborizations in its dendritic or axonal trees. Subsequently, building interactive visualizers to render individual neuronal graphs in real time relying on Blender ( Abdellah et al , 2018 ) is comparatively trivial. Blender is a powerful application; it comes with a rich high-level API and an intuitive GUI that can be exploited jointly for sketching, animation, mesh reconstruction and even progressive rendering using third party plug-ins such as Cycles.…”

Neuronize v2: Bridging the Gap Between Existing Proprietary Tools to Optimize Neuroscientific Workflows

“…According to the literature focusing on new 3D meshes generated from existing tracings, regarding the soma, some tools are based on the deformation approach proposed in Neuronize (Brito et al, 2013). For instance, NeuroMorphoVis (Abdellah et al, 2018) uses the same approach as the first version of Neuronize to generate a soma from incomplete information, whereas NeuroTessMesh (Garcia-Cantero et al, 2017) uses a similar method, but replaces the mass-spring deformation with a Finite Element Method deformation, which is easier to parametrize and generates a smoother membrane surface. However, none of these tools can make use of the new information about the soma present in Neurolucida files, which contain a set of 2D contours that Neuronize v2 uses to generate a more accurate soma.…”

“…Regarding the generation of the mesh, NeuroTessMesh (Garcia-Cantero et al, 2017) allows the visualization of complex scenarios with several neurons, reducing the resolution of the distant neurons, by using a GPU-based approach that allows dynamically-adaptive multiple levels of details (LOD) when visualizing the mesh. NeuroMorphoVis (Abdellah et al, 2018) first applies multiple processes to the input tracing to remove artifacts that can negatively affect the generated mesh. It then uses a new meshing algorithm that allows the generation of a unique neuron mesh from a set of watertight meshes with the advantage being that the mesh vertexes are related to the original tracing points.…”

“…There are several software tools that aim to facilitate the reconstructions of the neurons, employing different approaches-either extracting tracings from stacks of images, as is the case for Snake (Wang et al, 2011), APP2 (Xiao and Peng, 2013), flNeuronTool (Ming et al, 2013), SmartTracing (Chen et al, 2015), NeuTube (Feng et al, 2015), Rivulet (Liu et al, 2016), TreeMap (Zhou et al, 2016), NeuroGPS-Tree (Quan et al, 2016), Neuron tracer (Wang et al, 2017) and ShuTu (Jin et al, 2019); or processing neuronal tracings as occurs with Lasserre (Lasserre et al, 2012), Filament editor (Dercksen et al, 2014), NeuTube (Feng et al, 2015) and NeuroMorphoVis (Abdellah et al, 2018). Some tools use the neuronal tracings to create 3D meshes; for example, NeuroTessMesh (Garcia-Cantero et al, 2017), NeuroMorphoVis (Abdellah et al, 2018), Neuronize (Brito et al, 2013), and Lasserre (Lasserre et al, 2012). These tools facilitate the automatic tracing of neuronal structures and the correction of tracing errors to make reconstructions more accurate and efficient.…”

Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise Response

“…Single neuron morphology visualizations (Fig. 2b) were created using NeuroMorphoVis 73 . Figures were created using Matplotlib 74 .…”