Abstract
Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells’ overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes.
Highlights
Understanding the human brain remains one of the greatest research challenges for Science, being one of the most active areas of research
The extracted shape and the placement accuracy of the morphological points traced along the neuron contour or path are highly dependent on the quality of the obtained image stacks and on the expertise of the human operator, who is in charge of placing the morphological points within the neurites by manually clicking with a mouse, or by setting the parameters in automated algorithms
The goal of this module is to generate an initial low-poly mesh that approximates the whole neuron. This method is based on existing morphological tracings such as NeuroTessMesh those stored in NeuroMorpho
Summary
Understanding the human brain remains one of the greatest research challenges for Science, being one of the most active areas of research. Besides the intrinsic interest in understanding what makes us human, unraveling how the brain works will bring advances in many fields, from revolutionary computing technologies to the development of new treatments for brain disorders. There are different staining techniques, each of which is suited for particular experiments, and selecting the appropriate method is crucial to ensure a proper acquisition (Parekh and Ascoli, 2013) After any of these chemical staining processes, microscopes are able to capture the neuron morphology, including the somata, dendrites, and axons. Modern techniques such as multiphoton microscopy (Zipfel et al, 2003) automatically generate 3D image stacks of brain tissue, with image planes separated from each other by only a few micrometers. The extracted shape and the placement accuracy of the morphological points traced along the neuron contour or path are highly dependent on the quality of the obtained image stacks and on the expertise of the human operator, who is in charge of placing the morphological points within the neurites by manually clicking with a mouse, or by setting the parameters in (semi-) automated algorithms
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