Multi‐method analysis for the three‐dimensional reconstruction of muscle fascicles from <scp>DiceCT</scp> datasets
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".