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Example of brain data visualization of a healthy subject, consisting of a head mesh (semi-transparent), a brain MRI and a tractography dataset with labeled bundles (1% of the fibers displayed) (Dataset II). A An axial slice of the volume, all the bundles with cylinders, and the head mesh. B A sagittal slice, all the bundles with lines, and the head mesh. C An axial slice, some selected bundles with cylinders, and the head mesh
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Background
The visualization and analysis of brain data such as white matter diffusion tractography and magnetic resonance imaging (MRI) volumes is commonly used by neuro-specialist and researchers to help the understanding of brain structure, functionality and connectivity. As mobile devices are widely used among users and their technology shows a...
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Citations
... In the case of lines, the software loads the streamlines, defining a fixed normal per vertex, which corresponds to the normalized direction for the particular segment of the streamline. Furthermore, a Phong lighting algorithm (Osorio et al., 2021) is implemented in a vertex shader to compute the color of the streamline. The MRI data is rendered using specific shaders for slice visualization and volume rendering. ...
... This tool is quite useful when exploring brain tractography dataset quickly and in real-time. It was implemented with an optimal use of OpenGL features to perform well on personal computers, and even some simplified components can execute on Mobile devices (Osorio et al., 2021). Several libraries developed for diffusion MRI data analysis include tools for data visualization. ...
We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of tractography data is not straightforward due to the geometrical complexity of the streamlines, the file format, and the size of the datasets, which may contain millions of fibers. Hence, we collected and structured state-of-the-art methods for the analysis of tractography and packed them into a Python library, to integrate and share tools for tractography analysis. Due to the high computational requirements, the most demanding modules were implemented in C/C++. Available functions include brain Bundle Segmentation (FiberSeg), Hierarchical Fiber Clustering (HClust), Fast Fiber Clustering (FFClust), normalization to a reference coordinate system, fiber sampling, calculation of intersection between sets of brain fibers, tools for cluster filtering, calculation of measures from clusters, and fiber visualization. The library tools were structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis). Phybers is freely available on a GitHub repository under the GNU public license for non-commercial use and open-source development, which provides sample data and extensive documentation. In addition, the library can be easily installed on both Windows and Ubuntu operating systems through the pip library.
