A probabilistic framework for ultrasound image decomposition
Bibliographic record
Abstract
Image segmentation and tissue characterization are fundamental tasks of computer-aided diagnosis (CAD) in medical ultrasound imaging. As an initial step, such algorithms are usually based on extraction of pertinent features from the acquired ultrasound data. Typically, these features are computed directly from localized image segments, thereby representing local statistical properties of the image. However, the process of image formation of medical ultrasound suggests that such an approach could result in a variety of unwanted artifacts (such as excessively smooth segmentation boundaries or misclassification) at subsequent stages of the algorithm. In this work, we propose to first decompose the observed images into a number of their statistically distinct components. The decomposition is based on the maximum-a-posteriori (MAP) statistical framework which has been derived based on the signal and noise models appropriate for the ultrasound setting. Subsequently, each resulting component is used separately to extract a set of its corresponding features. When retrieved in this way (rather than directly from the observed image), the combined set of resulting features is shown to be capable of better discriminating between different tissue types. Examples of in silico simulations and in vivo experiments are provided to illustrate the practical usefulness of this technique for improving the results of ultrasound image segmentation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".