GoodFibes

GoodFibes reconstructs and detects muscle fibers from diffusible iodine-based contrast-enhanced computed tomography (diceCT) image stacks to quantify three-dimensional muscle architecture.

Key Features:

• Textural fiber tracking: Uses textural analysis of grayscale values to identify and track muscle fiber paths through diceCT image stacks.
• 3D reconstruction: Reconstructs muscle architecture in three dimensions from tracked fiber paths within diceCT stacks.
• Fiber morphology handling: Detects and follows both straight and curved muscle fiber trajectories across image slices.
• Post-processing functions: Provides functions for quality checking, fiber merging, 3D visualization, and exporting reconstructed fiber data.
• Statistical integration: Integrates with the R-language environment to compute and analyze fiber metrics such as mean fiber length.

Scientific Applications:

• Functional morphology: Non-destructive quantification of muscle architecture for studies of form and function using diceCT scans.
• Comparative and evolutionary analysis: Generation of fiber metrics suitable for comparative and evolutionary investigations of muscle anatomy.
• Method validation and case studies: Demonstrated on ant and bat diceCT datasets to produce mean fiber length measurements comparable to traditional methods.

Methodology:

Applies textural analysis of image grayscale values to identify and track straight and curved muscle fibers through diceCT image stacks and reconstructs those fibers in three dimensions.