Multi-method analysis for the three-dimensional reconstruction of muscle fascicles from DiceCT datasets

Muscle architecture is a major determinant of muscle performance and, in mammalian lineages, has been correlated with both feeding ecology and locomotor behaviors. Over the past decade, contrast-enhanced micro-CT (DiceCT) has emerged as an alternative to traditional dissection-based measurement. DiceCT allows the collection of myological data without damaging the specimen, and while preserving 3D relationships inside muscular tissues. However, manual segmentation of DiceCT datasets involves a major time investment and requires subjective judgments that can introduce bias. To address these shortcomings, several algorithmic approaches to tracing muscle fascicles have been described; however, these have not yet been rigorously tested in complex vertebrate muscle. Here, we present a standardized protocol for algorithmic fiber tracking using the commercial software extension XFiber within the Amira suite and compared its performance to manual segmentation, an open source algorithm (GoodFibes), and dissection values from the literature. Fascicle length and tortuosity (curvature) were measured in the jaw muscles of eight mammalian species spanning a wide range of cranial morphologies, diets, and body sizes. XFiber produced fascicle lengths that were generally similar to both gross dissection and manual segmentation, regardless of muscle identity or taxonomic group. All three digital methods tended to overestimate fascicle lengths relative to dissection, with XFiber and manual segmentation performing similarly (~15% overestimation). GoodFibes yielded substantially longer fascicle lengths, but its tortuosity values were closer to manual segmentation than those from XFiber. Given its major advantages in terms of time investment and inter-operator reliability, we suggest this workflow may represent a promising method for large-scale comparative studies.