A Feature-Based Morphing Methodology for In-Vivo Strain Assessment in Biological Structures
Bibliographic record
Abstract
It is often important, for diagnostic purposes, to evaluate quantitatively the motion undergone by a biological structure, starting from a viable tomographic imaging technique, such as Computed Tomography (CT) or Magnetic Resonance (MR). This is often associated with the need of the clinical personnel to perform an evaluation of a risk factor associated with pathology, e.g. ventricle performance [1]. As of today, the problem has been approached by using the magnetic resonance (MR) tagging technique [2] or experimentally by determining the location of a series of surgically implanted markers in each temporal phase [3]. The latter procedure is naturally very invasive, while MR tagging requires the use of sophisticated MR sequences that are rarely employed in standard care. Multi-detector row computer tomography (CT) scanners (routinely used in cardiac pathology diagnosis) as well as routine cine-MR sequences, on the other hand, allow dynamic imaging of the heart and large vessels with cardiac gating. In order to exploit these dynamic sequences, we have developed a tag-less method to estimate local strains from dynamic tomography gated images.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".