Phybers

Phybers provides a Python library for analyzing brain tractography data, integrating state-of-the-art methods for clustering, segmentation, and visualization of white matter streamlines.

Key Features:

• Bundle Segmentation: Implements FiberSeg for brain bundle segmentation of tractography streamlines.
• Hierarchical Clustering: Implements HClust for hierarchical fiber clustering of brain tractography data.
• Fast Clustering: Implements FFClust for fast fiber clustering of large tractography datasets.
• Normalization: Provides normalization to a reference coordinate system for tractography data.
• Fiber Sampling: Includes fiber sampling functionality for streamline datasets.
• Intersection Calculation: Calculates intersection between sets of brain fibers.
• Cluster Filtering: Provides tools for filtering clusters derived from tractography analysis.
• Cluster Measures: Calculates measures from clusters of brain fibers.
• Visualization: Includes Fibervis module for fiber visualization of tractography data.

Scientific Applications:

• White Matter Pathway Analysis: Analyzes 3D streamlines representing main white matter pathways in the brain.
• Large-Scale Tractography Processing: Handles datasets containing millions of fibers with computationally optimized C/C++ modules.

Methodology:

The most computationally demanding modules were implemented in C/C++. The library is structured into four principal modules: Segmentation, Clustering, Utils, and Visualization (Fibervis).