UltraTimTrack: a Kalman-filter-based algorithm to track muscle fascicles in ultrasound image sequences
Bibliographic record
Abstract
Abstract Background Brightness-mode (B-mode) ultrasound is a valuable tool to non-invasively image skeletal muscle architectural changes during movement, but automatically estimating architectural features such as fascicle length remains a major challenge. Existing fascicle tracking algorithms either require time-consuming drift corrections or yield noisy estimates that require post-processing. We therefore aimed to develop an algorithm that tracks fascicles without drift and noise across a range of experimental conditions and image acquisition settings. Methods We applied a Kalman filter to combine fascicle length and fascicle angle estimates from existing and openly available UltraTrack and TimTrack algorithms into a hybrid algorithm called UltraTimTrack. We applied the hybrid algorithm to ultrasound image sequences collected from the human medial gastrocnemius of healthy individuals ( N =8, 4 women), who performed cyclical submaximal plantar flexion contractions or remained at rest during passive ankle joint rotations at given frequencies and amplitudes whilst seated in a dynamometer chair. We quantified the algorithm’s tracking accuracy, noise, and drift as the respective mean, cycle-to-cycle, and accumulated between-contraction variability in fascicle length and fascicle angle. We expected UltraTimTrack’s estimates to be less noisy and to drift less across experimental conditions and image acquisition settings, compared with estimates from its parent algorithms. Results The proposed algorithm had low-noise estimates like UltraTrack and was drift-free like TimTrack across the broad range of conditions we tested. Estimated fascicle length and fascicle angle deviations accumulated to 2.1 ± 1.3 mm (mean ± s.d.) and 0.8 ± 0.7 deg, respectively, over 120 cyclical contractions. Average cycle-to-cycle variability was 1.4 ± 0.4 mm and 0.6 ± 0.3 deg, respectively. In comparison, UltraTrack had similar cycle-to-cycle variability (1.1 ± 0.3 mm, 0.5 ± 0.1 deg) but greater cumulative deviation (67.0 ± 59.3 mm, 9.3 ± 8.6 deg), whereas TimTrack had similar cumulative deviation (1.9 ± 2.2 mm, 0.9 ± 1.0 deg) but greater variability (3.5 ± 1.0 mm, 1.4 ± 0.5 deg). UltraTimTrack was significantly less affected by experimental conditions and image acquisition settings than its parent algorithms. It also performed well on a previously published image sequence from the human tibialis anterior, yielding a smaller root-mean-square deviation from manual tracking (fascicle length: 2.7 mm, fascicle angle: 0.7 deg) than a recently proposed hybrid algorithm (fascicle length: 4.5 mm, fascicle angle: 0.8 deg) and a machine-learning (DL_Track) algorithm (fascicle length: 8.2 mm, fascicle angle: 4.8 deg). Conclusion We developed a Kalman-filter-based method to improve fascicle tracking from B-mode ultrasound image sequences. The proposed algorithm provides low-noise, drift-free estimates of muscle architectural changes that may better inform muscle function interpretations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".