A roadmap to reconstructing muscle architecture from CT data

Skeletal muscle is responsible for voluntary force generation across
animals, and muscle architecture largely determines the parameters of
mechanical output. The ability to analyze muscle performance through
muscle architecture is thus a key step towards better understanding the
ecology and evolution of movements and morphologies. In pennate skeletal
muscle, volume, fiber lengths and attachment angles to force transmitting
structures comprise the most relevant parameters of muscle architecture.
Measuring these features through tomographic techniques offers an
alternative to tedious and destructive dissections, particularly as the
availability of tomographic data is rapidly increasing.  However,
there is a need for streamlined computational methods to access this
information efficiently. Here, we establish and compare workflows using
partially automated image analysis for fast and accurate estimation of
animal muscle architecture. After isolating a target muscle through
segmentation, we evaluate freely available and proprietary fiber tracing
algorithms to reconstruct muscle fibers. We then present a script using
the Blender Python API to estimate attachment angles, fiber lengths,
muscle volume and Physiological Cross-Sectional Area. We apply these
methods to insect and vertebrate muscle and provide guided workflows.
Results from fiber tracing are consistent compared to manual measurements
but much less time-consuming. Lastly, we emphasize the capabilities of the
open-source 3D software Blender as both a tool for visualization and a
scriptable analytic tool to process digitized anatomical data. Across
organisms, it is feasible to extract, analyze, and visualize muscle
architecture from tomography data by exploiting the spatial features of
scans and the geometric properties of muscle fibers. As digital libraries
of anatomies continue to grow, the workflows and approach presented here
can be part of the open-source future of digital comparative
analysis.