Computerised system for measurement of muscle thickness based on ultrasonography
Bibliographic record
Abstract
In this paper, a computerised system for measuring muscle thicknesses of the transverse abdominus (TrA), internal oblique and external oblique muscles based on ultrasonography is presented. The system is designed to allow for quantitative analysis of changes in muscle recruitment and activity, which facilitates the study of such changes and its relationship with low back pain. The abdominal muscle area was localised and imaged under different standing conditions using B-mode ultrasonography. To account for issues such as misalignments due to probe and subject motion as well as speckle noise inherent to ultrasonography, automatic ensemble registration is performed on the acquired images using a sequential quadratic programming approach based on a novel log-Rayleigh likelihood function. Regions of interest are then automatically identified based on the medial border of the TrA for the purpose of quantitative muscle thickness measurements. Experimental results show that the proposed system achieves registration errors of under 0.4 mm when compared with ground-truth measurements, as well as allow for the measurement of muscle thickness changes in the millimetre range. The proposed system is currently in operational use as an analysis tool for studying the relationship between abdominal muscle thickness changes and postural changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".