Quantifying muscle architecture in embryos using <scp>diceCT</scp> and algorithmic fascicle tracking
Bibliographic record
Abstract
Advances in soft-tissue imaging and muscle reconstruction tools have greatly expanded our capacity to extract myological properties relating to function. Recently, the development of semi-autonomous fascicle tracking algorithms has permitted in situ measurements of fiber lengths and orientation. While these tools have been applied to postnatal, predominantly adult vertebrate specimens, their efficacy has not been demonstrated on embryonic specimens, which possess smaller and less developed muscle tissues. If fascicle tracking algorithms could be extended successfully to embryonic specimens, then life history changes to muscle action and function could be recorded in situ and in high fidelity from the onset of muscle contraction. In this study, we present a successful implementation of a fascicle tracking tool on jaw adductor and depressor muscles in a domestic chick embryo (Gallus gallus domesticus). Comparisons of algorithmic and manual fascicle reconstructions show visual and quantitative validation of the protocol. Compared with results from adult chickens, jaw muscles in embryos were not as uniformly oriented, and the muscles that close the jaw had relatively small physiological cross-sectional areas. This result implies that the growth trajectory is influenced by feeding requirements, such as bite force. We also report an artifact with the fascicle tracking method, where fascicle lengths appear shorter in smaller, thinner muscles relative to measurements based on manual segmentation of the image data. Nevertheless, fascicle orientations are congruent with those extracted from manual segmentation, even for the smallest muscles. Taken together, we demonstrate that an existing tool for semi-automated fascicle tracking is extensible to embryonic specimens. As such, the approach presented here paves a new path for investigating form-function relationships and the effect of muscle action on other tissues, such as bone, from the earliest stages of muscle contractions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".