Cardiac motion assessment from echocardiographic image sequences by means of the structure multivector
Bibliographic record
Abstract
We recently contributed an algorithm for the estimation of cardiac deformation from echocardiographic image sequences based on the monogenic signal. By exploiting the phase information instead of the pixel intensity, the algorithm was robust to the temporal contrast variations normally encountered in cardiac ultrasound. In this paper we propose an improvement of that framework making use of an extension of the monogenic signal formalism, called structure multivector. The structure multivector models the image as the superposition of two perpendicular waves with associated amplitude, phase and orientation. Such a model is well adapted to describe the granular pattern of the characteristic speckle noise. The displacement is computed by solving the optical flow equation jointly for the two image phases. A local affine model accounts for typical cardiac motions as contraction/expansion and shearing; a coarse-to-fine B-spline scheme allows for a robust and effective computation of the model parameters and a pyramidal refinement scheme helps deal with large motions. Performance was evaluated on realistic simulated cardiac ultrasound sequences and compared to our previous monogenic-based algorithm and classical speckle tracking. Endpoint-error was used as accuracy metric. With respect to them we achieved error reductions of 13% and 30% respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".