Toward Real‐Time Backscatter Coefficient Estimation Incorporating the U‐Net Segmentation and an In Vivo Reference Target
Bibliographic record
Abstract
Quantitative ultrasound using spectral-based techniques, like the backscatter coefficient (BSC), have demonstrated capabilities for tumor characterization and therapy monitoring. The incorporation of an in situ calibration target, that is, a small titanium bead, can provide more consistent BSC estimates. For analyzing tumors, BSC estimation traditionally relies on manual tumor segmentation and calibration bead detection, a time-consuming and skill-dependent task. This study utilizes a U-Net model for automatic BSC estimation by integrating identification of a titanium calibration target embedded in rabbit mammary tumors with automatic segmentation, enabling real-time applications. The U-Net model demonstrated strong segmentation performance, achieving a Dice score of 0.86. Performance metrics demonstrated reliable BSC parameter estimation, with relative errors of 17.87% for effective scatter diameter (ESD) and 9.95% for effective attenuation concentration (EAC) when comparing automated segmentation to manual segmented tumors, highlighting its potential for accurate, real-time tumor diagnostics and therapy monitoring in clinical practice.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".