Ultrasound imaging dataset for elastography, deformation correction, and algorithm evaluation
Bibliographic record
Abstract
Ultrasound imaging is widely used for its cost-effectiveness, non-invasiveness, and real-time capabilities in diagnostics and image-guided procedures. However, the direct skin contact involved in ultrasound image acquisition induces deformations in the tissues. In certain scenarios, such as elastography or freehand 3D ultrasound imaging, the relative deformations between successive ultrasound images must therefore be estimated or corrected. This is a non-rigid image registration operation. In addition to image data, validation requires knowledge of several parameters related to image deformation, such as probe contact force, indentation or mechanical parameters of the imaged body. Existing methods are often validated on simulated data or non-public datasets accessible within the research group, and the reported performances are challenging to reproduce by other research groups. Quality public datasets would enable research groups to compare their research results and drive performance improvements through competition. To address this gap, we introduce a publicly available dataset with controlled deformations on a phantom, including B-mode images, RF signals, probe indentations, contact force, and phantom material mechanical parameters. This dataset serves as a valuable resource for validating various registration and elastography methods, offering a standardized platform for performance evaluation and comparison among research groups. The article outlines the experimental protocol, the mechanical system for probe maintenance and indentation, force measurement, and the phantom creation process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".